diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e51072b23e38531e131588109bfcb6ac6667e155
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29253, "bytes_utf8": 29253, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 237, "bytes_utf8": 237, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 220, "reasoning_output_tokens": 154}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..24293c6e72c6e280c0d36a411c0b10f1569454d8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_01bf1c58a49bee59",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 19840,
+    "output_tokens": 220,
+    "total_tokens": 20506,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 26288.86,
+    "sql_execution_elapsed_ms_total": 2.48,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d1c35db1b645a9f71a1ec043ebe6bb3fe48343d1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:33:11.419557+00:00",
+  "ended_at": "2026-05-19T15:33:37.708461+00:00",
+  "elapsed_ms": 26288.86,
+  "prompt_metrics": {
+    "chars": 29253,
+    "bytes_utf8": 29253,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2248,
+    "bytes_utf8": 2248,
+    "lines": 9,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 237,
+      "bytes_utf8": 237,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20286,
+      "cached_input_tokens": 19840,
+      "output_tokens": 220,
+      "reasoning_output_tokens": 154
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4db1394ae5449a4c932307d7c0c729f08b032030
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
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+      "word_freq_business": "0.07",
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+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
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+      "word_freq_meeting": "0",
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+      "word_freq_re": "0",
+      "word_freq_edu": "0",
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+      "char_freq_%5B": "0",
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+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
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+      "word_freq_3d": "0",
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+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
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+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
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+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
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+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
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+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..68d6d8b80172c89770687e2a6bab8ef57d70a566
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,9 @@
+{"type":"thread.started","thread_id":"019e40de-9d8c-7c12-9ecd-04623ca51b1d"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1997605, Requested 20921. Please try again in 555ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 1/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990463, Requested 20921. Please try again in 341ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS row_count\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Counts rows for each value of \\\"class\\\" to inspect the class distribution.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":19840,"output_tokens":220,"reasoning_output_tokens":154}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..88366d61e21cb7ed678df7f249d6981fb89db547
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS row_count\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY row_count DESC;","notes":"Counts rows for each value of \"class\" to inspect the class distribution."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..fb1633fdadfa268a63caf3014d1730a6959bac24
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:30.469248+00:00",
+  "ended_at": "2026-05-19T16:01:33.417284+00:00",
+  "elapsed_ms": 2948.01,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29304,
+    "bytes_utf8": 29304,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2eeb3d5eb292bbf93ac690d2e3129bf5b5f34ed0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:34.420466+00:00",
+  "ended_at": "2026-05-19T16:01:37.616337+00:00",
+  "elapsed_ms": 3195.82,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29304,
+    "bytes_utf8": 29304,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db55cafb3aa206de9c97ad49a94fe03788efd209
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "word_freq_address",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db55cafb3aa206de9c97ad49a94fe03788efd209
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "word_freq_address",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d854321c4c8ba266ac6a11d5ee89c6bac27f5f8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-8a61-75f3-92fd-c0829756a48e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2474230365d1e783972ceda6b3e50a0668e6c23f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-8a61-75f3-92fd-c0829756a48e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9712255cb89d9029744b0bc93937d98da9e0ce91
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-99d2-75b1-a421-a886202b5193"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5f0cf252985e95b9917db9351aece81515f1e013
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-99d2-75b1-a421-a886202b5193"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..405eceb3de3bb0c3ceee5c721b8d0deb00cbdd40
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/run_manifest.json
@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:01:30.467285+00:00",
+  "ended_at": "2026-05-19T16:01:37.617251+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_03957dab4b318bed",
+    "problem_id": "v2p_n1_e25e5b5ae21d3cf2",
+    "dataset_id": "n1",
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+    "bindings": {
+      "measure_col": "word_freq_address",
+      "top_k": 10,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=1/1",
+      "binding_index=115"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 10,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f77ca42be9af8e09a3211beeb6f43962b4c52bec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:01:33.418088+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2948.01, "started_at": "2026-05-19T16:01:30.469248+00:00", "ended_at": "2026-05-19T16:01:33.417284+00:00", "prompt_metrics": {"chars": 29304, "bytes_utf8": 29304, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f8-8a61-75f3-92fd-c0829756a48e\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:01:37.617154+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3195.82, "started_at": "2026-05-19T16:01:34.420466+00:00", "ended_at": "2026-05-19T16:01:37.616337+00:00", "prompt_metrics": {"chars": 29304, "bytes_utf8": 29304, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f8-99d2-75b1-a421-a886202b5193\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81658ed158806d41fb36071cff7b1292761838f6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29525, "bytes_utf8": 29525, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29525, "bytes_utf8": 29525, "lines": 792, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 450, "bytes_utf8": 450, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 12032, "output_tokens": 329, "reasoning_output_tokens": 207}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..2fe1313a59fefc331af6c3d73cd586fb190a04ba
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_05786dfc8dc90728",
+    "api_calls": 0,
+    "input_tokens": 20359,
+    "cached_input_tokens": 12032,
+    "output_tokens": 329,
+    "total_tokens": 20688,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11670.96,
+    "sql_execution_elapsed_ms_total": 7.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc38dbf68e23a4ee2cc646d499766742418fe8e5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:51.033297+00:00",
+  "ended_at": "2026-05-19T16:04:53.995292+00:00",
+  "elapsed_ms": 2961.97,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5bec6e1c19efeecb0d59334e3dca3c7a31e88de2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:54.997365+00:00",
+  "ended_at": "2026-05-19T16:05:03.706403+00:00",
+  "elapsed_ms": 8708.99,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 810,
+    "bytes_utf8": 810,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 450,
+      "bytes_utf8": 450,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20359,
+      "cached_input_tokens": 12032,
+      "output_tokens": 329,
+      "reasoning_output_tokens": 207
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c57b9dc02093358d04e2aa624c3eabece5a29ac2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c57b9dc02093358d04e2aa624c3eabece5a29ac2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..33104670902497e7a208eed6366aaee1b6dbf45c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-99fd-7642-b681-b4ddb8fde85a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad8c078e835a1f3c9c0c23f59ff76694719b1fc5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-99fd-7642-b681-b4ddb8fde85a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..367df24bd2a511fe8001022a66f5bea91b546064
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-a979-7543-afd3-3440f83bdb76"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_font\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_font\\\" AS REAL)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 15;\",\"notes\":\"Used the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_font\\\". CAST to REAL is included because the schema stores these numeric features as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20359,"cached_input_tokens":12032,"output_tokens":329,"reasoning_output_tokens":207}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f5a8198cebd4e287e522ab823f8f365aa8857162
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_font\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_font\" AS REAL)) > 0.0\nORDER BY total_measure DESC\nLIMIT 15;","notes":"Used the required template with group_col=\"class\" and measure_col=\"word_freq_font\". CAST to REAL is included because the schema stores these numeric features as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1fb41bb2acd917d1a418b2bfec5688d7e03c7c15
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%3B.
+Result preview: [{"class": "0", "avg_measure": 0.050280846484935436}, {"class": "1", "avg_measure": 0.020573083287369003}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..ad812d1f1cdf75023c4fb1dea1dc88fcd7028962
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_06a5b861b6b26a1c
+-- problem_id: v2p_n1_4f1bfaf50ec0d80a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("char_freq_%3B" AS REAL)) OVER (PARTITION BY "class") AS "avg_measure"
+FROM "n1"
+ORDER BY "avg_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dcb9247abf5f77d75c3ebd942cb46d2a1a928e9b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%3B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.050280846484935436}, {\"class\": \"1\", \"avg_measure\": 0.020573083287369003}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.27}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..00a30a3deab6f5cb129a258cf4f07ebd5298e8c1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:41:15.530686+00:00",
+  "ended_at": "2026-05-19T15:41:26.750416+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_06a5b861b6b26a1c",
+    "problem_id": "v2p_n1_4f1bfaf50ec0d80a",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%3B.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%3B",
+      "top_k": 13,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=48"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_06a5b861b6b26a1c.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_06a5b861b6b26a1c",
+    "api_calls": 0,
+    "input_tokens": 20331,
+    "cached_input_tokens": 19840,
+    "output_tokens": 505,
+    "total_tokens": 20836,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11207.34,
+    "sql_execution_elapsed_ms_total": 8.27,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81aecf24cd6b35edb5f6d97b5ac3062b0fcab8b3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:41:26.740799+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11207.34, "started_at": "2026-05-19T15:41:15.532632+00:00", "ended_at": "2026-05-19T15:41:26.740024+00:00", "prompt_metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 430, "bytes_utf8": 430, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20331, "cached_input_tokens": 19840, "output_tokens": 505, "reasoning_output_tokens": 390}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Uses the planned Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%3B\\\". CAST to REAL is added because the schema snapshot shows the measure column stored as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..0376ac223261d803c71fb89bb03e240f3ece6404
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_06a5b861b6b26a1c",
+  "api_calls": 0,
+  "input_tokens": 20331,
+  "cached_input_tokens": 19840,
+  "output_tokens": 505,
+  "total_tokens": 20836,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11207.34,
+  "sql_execution_elapsed_ms_total": 8.27,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0c8684565dbc8cb33ec67e7acd585665776098a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_you.
+Result preview: [{"class": "1", "total_measure": 4105.61}, {"class": "0", "total_measure": 3541.71}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..5be2abc886818e079e346e7fda49db86d5e9c7d2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/generated_sql.sql
@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_tpch_thresholded_group_ranking
+-- query_record_id: v2q_n1_06f410de776111c5
+-- problem_id: v2p_n1_8b35fa14b920400b
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_you" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+HAVING SUM(CAST("word_freq_you" AS REAL)) > 2.64
+ORDER BY total_measure DESC
+LIMIT 12;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5113b9f5cd2ae5a8f7926a01e84fec7d777b43c8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_you\" AS REAL)) > 2.64\nORDER BY total_measure DESC\nLIMIT 12;", "result": "{\"query\": \"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.64\\nORDER BY total_measure DESC\\nLIMIT 12;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 4105.61}, {\"class\": \"0\", \"total_measure\": 3541.71}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.25}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a3a2fa11ffc9d567d299ac9bc077ce4646b3347
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:03:47.788513+00:00",
+  "ended_at": "2026-05-19T16:03:59.776060+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_06f410de776111c5",
+    "problem_id": "v2p_n1_8b35fa14b920400b",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_you.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_you",
+      "top_k": 12,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 2.64,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=132"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_06f410de776111c5.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_06f410de776111c5",
+    "api_calls": 0,
+    "input_tokens": 20360,
+    "cached_input_tokens": 12032,
+    "output_tokens": 544,
+    "total_tokens": 20904,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11979.35,
+    "sql_execution_elapsed_ms_total": 3.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54998e58198d68e9bed12d6ceb2ff667eb4a8e25
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:03:59.771043+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11979.35, "started_at": "2026-05-19T16:03:47.790570+00:00", "ended_at": "2026-05-19T16:03:59.769951+00:00", "prompt_metrics": {"chars": 29522, "bytes_utf8": 29522, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 470, "bytes_utf8": 470, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20360, "cached_input_tokens": 12032, "output_tokens": 544, "reasoning_output_tokens": 419}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.64\\nORDER BY total_measure DESC\\nLIMIT 12;\",\"notes\":\"Used the required Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..22c4efc4c6ffe7fd51b9e7ff911c9ff2e949917e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_06f410de776111c5",
+  "api_calls": 0,
+  "input_tokens": 20360,
+  "cached_input_tokens": 12032,
+  "output_tokens": 544,
+  "total_tokens": 20904,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11979.35,
+  "sql_execution_elapsed_ms_total": 3.25,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afaeda89f21de29cdae2f90f63691cb3bc7b4d61
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29767, "bytes_utf8": 29767, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 621, "bytes_utf8": 621, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 461, "reasoning_output_tokens": 288}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..52fbb2aee191ac8d115d1e0a6f50934c1d3d01f9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_0846f22fe82fa8a6",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 461,
+    "total_tokens": 20898,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18779.78,
+    "sql_execution_elapsed_ms_total": 4.41,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..574a98bed65d4f6e95aaf4456869c64b439fe685
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:34:21.443511+00:00",
+  "ended_at": "2026-05-19T15:34:40.223317+00:00",
+  "elapsed_ms": 18779.78,
+  "prompt_metrics": {
+    "chars": 29767,
+    "bytes_utf8": 29767,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1667,
+    "bytes_utf8": 1667,
+    "lines": 6,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 621,
+      "bytes_utf8": 621,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 12032,
+      "output_tokens": 461,
+      "reasoning_output_tokens": 288
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bb5955231ce21070b5c3bd8692badf39c3e2eab8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_hpl.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hpl",
+    "item_col": "word_freq_george",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2edbd667f1cbe952de68f933efb62b6c00e86ff0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,6 @@
+{"type":"thread.started","thread_id":"019e40df-af2d-7722-8d75-c068294916c5"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1985816, Requested 21072. Please try again in 206ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993331, Requested 21072. Please try again in 432ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_george\\\",\\n       SUM(CAST(\\\"word_freq_hpl\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_hpl\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hpl\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_george\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"class\\\", item_col=\\\"word_freq_george\\\", and measure_col=\\\"word_freq_hpl\\\". Cast the measure column to REAL because the schema stores feature values as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":12032,"output_tokens":461,"reasoning_output_tokens":288}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ceddb9ab2ab5cf8a052e7eb5972e47816c7e55df
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_george\",\n       SUM(CAST(\"word_freq_hpl\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_hpl\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_hpl\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_george\"\nORDER BY share_within_group DESC;","notes":"Applied the provided template with group_col=\"class\", item_col=\"word_freq_george\", and measure_col=\"word_freq_hpl\". Cast the measure column to REAL because the schema stores feature values as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0a8c86c9d83e77ec31a01541717a9b2dc238a9b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%21.
+Result preview: [{"class": "1", "avg_measure": 0.5137126309983453}, {"class": "0", "avg_measure": 0.10998350071736011}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..d042ee68d1071225a58287491d24fbd008f5a652
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_0c626140449240f5
+-- problem_id: v2p_n1_ba33556e7ae2c3a5
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "class",
+       AVG(CAST("char_freq_%21" AS REAL)) OVER (PARTITION BY "class") AS "avg_measure"
+FROM "n1"
+ORDER BY "avg_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4725eeb0506c2f69d05f0409f08f89b1874eef1c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%21\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%21\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"1\", \"avg_measure\": 0.5137126309983453}, {\"class\": \"0\", \"avg_measure\": 0.10998350071736011}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 17.71}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..8fd61cb268808b9f46468d9ea37fe9aaff309f4d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:42:51.824821+00:00",
+  "ended_at": "2026-05-19T15:43:03.789817+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_0c626140449240f5",
+    "problem_id": "v2p_n1_ba33556e7ae2c3a5",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%21.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%21",
+      "top_k": 16,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.171,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=2/2",
+      "binding_index=51"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_0c626140449240f5.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_0c626140449240f5",
+    "api_calls": 0,
+    "input_tokens": 20328,
+    "cached_input_tokens": 19840,
+    "output_tokens": 279,
+    "total_tokens": 20607,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11940.84,
+    "sql_execution_elapsed_ms_total": 17.71,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbe2498b3fdbca14312bb0b393e080ad809cfc71
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:43:03.769525+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11940.84, "started_at": "2026-05-19T15:42:51.826972+00:00", "ended_at": "2026-05-19T15:43:03.767841+00:00", "prompt_metrics": {"chars": 29442, "bytes_utf8": 29442, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 420, "bytes_utf8": 420, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 19840, "output_tokens": 279, "reasoning_output_tokens": 171}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%21\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%21\\\". Cast the measure to REAL because the schema stores feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ae242ba72a71f1bb3e6d6a86eda982734caa92b2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_0c626140449240f5",
+  "api_calls": 0,
+  "input_tokens": 20328,
+  "cached_input_tokens": 19840,
+  "output_tokens": 279,
+  "total_tokens": 20607,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11940.84,
+  "sql_execution_elapsed_ms_total": 17.71,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c1ae7188ac8cddeeeb6c5bce9aaf8c4027015832
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.
+Result preview: [{"class": "1", "total_measure": 298.55}, {"class": "0", "total_measure": 2.47}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..64c493991b75b1c6396a4330e4a40158fdf38cd9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_rank_order
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_12e9d7ee8ce2ca10
+-- problem_id: v2p_n1_eb62697dcfdacc00
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_3d" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+ORDER BY total_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..777231688d2e203ac9b37341d667f79be9b0e493
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_3d\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 298.55}, {\"class\": \"0\", \"total_measure\": 2.47}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.22}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..a0176af66f6ae1e6127c6723e016ce17516490ff
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:29:23.657851+00:00",
+  "ended_at": "2026-05-19T15:29:34.911144+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_12e9d7ee8ce2ca10",
+    "problem_id": "v2p_n1_eb62697dcfdacc00",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_rank_order",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_3d",
+      "top_k": 18,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=2/2",
+      "binding_index=3"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_12e9d7ee8ce2ca10.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_12e9d7ee8ce2ca10",
+    "api_calls": 0,
+    "input_tokens": 20319,
+    "cached_input_tokens": 12032,
+    "output_tokens": 303,
+    "total_tokens": 20622,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11245.74,
+    "sql_execution_elapsed_ms_total": 2.22,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e09a2ebb2860de8baae48d5e611b4e08823f3b7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:29:34.907569+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11245.74, "started_at": "2026-05-19T15:29:23.660355+00:00", "ended_at": "2026-05-19T15:29:34.906127+00:00", "prompt_metrics": {"chars": 29354, "bytes_utf8": 29354, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 360, "bytes_utf8": 360, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20319, "cached_input_tokens": 12032, "output_tokens": 303, "reasoning_output_tokens": 203}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_3d\\\". CAST to REAL is used because the schema stores feature columns as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..448be2fdce87cc78873b667f6f03012957cf1013
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_12e9d7ee8ce2ca10",
+  "api_calls": 0,
+  "input_tokens": 20319,
+  "cached_input_tokens": 12032,
+  "output_tokens": 303,
+  "total_tokens": 20622,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11245.74,
+  "sql_execution_elapsed_ms_total": 2.22,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1270c49b8ce8633608028058598d72714bd828e7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:23.654229+00:00",
+  "ended_at": "2026-05-19T16:02:26.998789+00:00",
+  "elapsed_ms": 3344.53,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29335,
+    "bytes_utf8": 29335,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..4b78d5d4d3fa6e0c3713599c5bd6b1cf7a49dadb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:28.001832+00:00",
+  "ended_at": "2026-05-19T16:02:31.625295+00:00",
+  "elapsed_ms": 3623.42,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29335,
+    "bytes_utf8": 29335,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7f5f61c7ccfb1468d11ef3b6f80d87ad3984b9f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 17,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7f5f61c7ccfb1468d11ef3b6f80d87ad3984b9f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 17,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..34bb40460dd22d810944a6bd5e29e8bb86e9d7fc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-5a36-7402-ab88-637ecfc6798e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b5522acf8974ec494820fb0f07e327a0d5760b8e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-5a36-7402-ab88-637ecfc6798e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bb1c8b2bd5d73ffab1f5427a2eeb4081c7a1e62f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-6b32-7c61-8cd1-4af77c1f7657"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dcd6561f9e6820667f9fb34748e5619a44834ce1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-6b32-7c61-8cd1-4af77c1f7657"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1437cbb14dabec2c/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6d6d639916db5b1e3deaaabd106f2bd572d3031
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_george.
+Result preview: [{"class": "1", "word_freq_650": "0", "total_measure": 2.81, "share_within_group": 100.0}, {"class": "0", "word_freq_650": "0", "total_measure": 3156.36, "share_within_group": 89.47714567576455}, {"class": "0", "word_freq_650": "4.76", "total_measure": 28.56, "share_within_group": 0.8096247831362188}, {"class": "0", "word_freq_650": "4.34", "total_measure": 13.02, "share_within_group": 0.36909365113562914}, {"class": "0", "word_freq_650": "4.16", "total_measure": 12.48, "share_within_group": 0.35378561952170906}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..6dcdbdd9ed8952a04230db4bb4de2cfce38090ac
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_15f59192bc30161b
+-- problem_id: v2p_n1_80d32cfec1b56e77
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "class",
+  "word_freq_650",
+  SUM(CAST("word_freq_george" AS REAL)) AS total_measure,
+  SUM(CAST("word_freq_george" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST("word_freq_george" AS REAL))) OVER (PARTITION BY "class"), 0.0) AS share_within_group
+FROM "n1"
+GROUP BY "class", "word_freq_650"
+ORDER BY share_within_group DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55fbd4c8ca9335cad699a5f365ae0090f51953d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_650\",\n  SUM(CAST(\"word_freq_george\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_george\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\"word_freq_george\" AS REAL))) OVER (PARTITION BY \"class\"), 0.0) AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_650\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_650\\\",\\n  SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0.0) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"class\", \"word_freq_650\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_650\": \"0\", \"total_measure\": 2.81, \"share_within_group\": 100.0}, {\"class\": \"0\", \"word_freq_650\": \"0\", \"total_measure\": 3156.36, \"share_within_group\": 89.47714567576455}, {\"class\": \"0\", \"word_freq_650\": \"4.76\", \"total_measure\": 28.56, \"share_within_group\": 0.8096247831362188}, {\"class\": \"0\", \"word_freq_650\": \"4.34\", \"total_measure\": 13.02, \"share_within_group\": 0.36909365113562914}, {\"class\": \"0\", \"word_freq_650\": \"4.16\", \"total_measure\": 12.48, \"share_within_group\": 0.35378561952170906}, {\"class\": \"0\", \"word_freq_650\": \"2.04\", \"total_measure\": 8.67, \"share_within_group\": 0.245778952023495}, {\"class\": \"0\", \"word_freq_650\": \"0.5\", \"total_measure\": 8.4, \"share_within_group\": 0.23812493621653494}, {\"class\": \"0\", \"word_freq_650\": \"2.77\", \"total_measure\": 6.92, \"share_within_group\": 0.19616959031171688}, {\"class\": \"0\", \"word_freq_650\": \"2.63\", \"total_measure\": 6.13, \"share_within_group\": 0.1737745070246856}, {\"class\": \"0\", \"word_freq_650\": \"1.01\", \"total_measure\": 6.06, \"share_within_group\": 0.1717901325562145}, {\"class\": \"0\", \"word_freq_650\": \"0.86\", \"total_measure\": 6.05, \"share_within_group\": 0.17150665048929006}, {\"class\": \"0\", \"word_freq_650\": \"4\", \"total_measure\": 6.0, \"share_within_group\": 0.1700892401546678}, {\"class\": \"0\", \"word_freq_650\": \"5.88\", \"total_measure\": 5.88, \"share_within_group\": 0.16668745535157445}, {\"class\": \"0\", \"word_freq_650\": \"0.63\", \"total_measure\": 5.7299999999999995, \"share_within_group\": 0.16243522434770777}, {\"class\": \"0\", \"word_freq_650\": \"0.76\", \"total_measure\": 5.34, \"share_within_group\": 0.15137942373765437}, {\"class\": \"0\", \"word_freq_650\": \"0.66\", \"total_measure\": 5.32, \"share_within_group\": 0.15081245960380546}, {\"class\": \"0\", \"word_freq_650\": \"1.31\", \"total_measure\": 5.26, \"share_within_group\": 0.1491115672022588}, {\"class\": \"0\", \"word_freq_650\": \"0.68\", \"total_measure\": 5.130000000000001, \"share_within_group\": 0.14542630033224102}, {\"class\": \"0\", \"word_freq_650\": \"0.58\", \"total_measure\": 4.9799999999999995, \"share_within_group\": 0.14117406932837429}, {\"class\": \"0\", \"word_freq_650\": \"0.8\", \"total_measure\": 4.8100000000000005, \"share_within_group\": 0.13635487419065873}, {\"class\": \"0\", \"word_freq_650\": \"1.28\", \"total_measure\": 4.69, \"share_within_group\": 0.13295308938756537}, {\"class\": \"0\", \"word_freq_650\": \"3.12\", \"total_measure\": 4.68, \"share_within_group\": 0.1326696073206409}, {\"class\": \"0\", \"word_freq_650\": \"2.32\", \"total_measure\": 4.64, \"share_within_group\": 0.13153567905294308}, {\"class\": \"0\", \"word_freq_650\": \"4.54\", \"total_measure\": 4.54, \"share_within_group\": 0.12870085838369866}, {\"class\": \"0\", 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diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..aaceff33ee4de007ee7df6398d7bc3e9e8199a78
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:34:55.646318+00:00",
+  "ended_at": "2026-05-19T15:35:11.275320+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_15f59192bc30161b",
+    "problem_id": "v2p_n1_80d32cfec1b56e77",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_george.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_george",
+      "item_col": "word_freq_650",
+      "top_k": 11,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=26"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_15f59192bc30161b.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_15f59192bc30161b",
+    "api_calls": 0,
+    "input_tokens": 20438,
+    "cached_input_tokens": 19840,
+    "output_tokens": 711,
+    "total_tokens": 21149,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15617.43,
+    "sql_execution_elapsed_ms_total": 5.93,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1b8e753c5d46d98aade90c52d9d2eaf8ca6a6de
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:35:11.267133+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15617.43, "started_at": "2026-05-19T15:34:55.648488+00:00", "ended_at": "2026-05-19T15:35:11.265953+00:00", "prompt_metrics": {"chars": 29775, "bytes_utf8": 29775, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 667, "bytes_utf8": 667, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 19840, "output_tokens": 711, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_650\\\",\\n  SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0.0) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_650\\\", and measure_col=\\\"word_freq_george\\\". Cast the measure from TEXT to REAL for SQLite numeric aggregation and used NULLIF to avoid divide-by-zero.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8730b318521073864db4490974b54406c01843b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_15f59192bc30161b",
+  "api_calls": 0,
+  "input_tokens": 20438,
+  "cached_input_tokens": 19840,
+  "output_tokens": 711,
+  "total_tokens": 21149,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 15617.43,
+  "sql_execution_elapsed_ms_total": 5.93,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b979c715d87ea47090ce947b03edac4885545c4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 299, "bytes_utf8": 299, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20324, "cached_input_tokens": 12032, "output_tokens": 260, "reasoning_output_tokens": 172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1e6752c0b3896c5295d9238fcac3c0d5fdbb3a9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_18238bc3d7c3a054",
+    "api_calls": 0,
+    "input_tokens": 20324,
+    "cached_input_tokens": 12032,
+    "output_tokens": 260,
+    "total_tokens": 20584,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11711.98,
+    "sql_execution_elapsed_ms_total": 2.34,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..290fa79a0343b2ad672433ffbde36cd1214c27db
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:10.932239+00:00",
+  "ended_at": "2026-05-19T16:02:14.334092+00:00",
+  "elapsed_ms": 3401.83,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa27100bb9159a0f6f27c869eff0af855dfcb64a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:15.336112+00:00",
+  "ended_at": "2026-05-19T16:02:23.646299+00:00",
+  "elapsed_ms": 8310.15,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 656,
+    "bytes_utf8": 656,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 299,
+      "bytes_utf8": 299,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20324,
+      "cached_input_tokens": 12032,
+      "output_tokens": 260,
+      "reasoning_output_tokens": 172
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..84243f88ec203aee7677b75087bc966e5d896509
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 12,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..84243f88ec203aee7677b75087bc966e5d896509
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
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+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
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+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
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+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 12,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19efa9238034967f9d25dc860def8828d80d4754
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-289c-7dd1-8251-d500d7ad979e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b90af6e0cd0a8fcf2488d9022b025a67dd41efa2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-289c-7dd1-8251-d500d7ad979e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cc9edfb242b9f15856a35d475e8190bf5b97b46c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-39a6-7360-9ad8-5ae291c71055"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"class\\\",\\n    COUNT(*) AS support\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY support ASC, \\\"class\\\"\\nLIMIT 12;\",\"notes\":\"Uses the required Low-Support Group Count template with group_col bound to \\\"class\\\" on table \\\"n1\\\".\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20324,"cached_input_tokens":12032,"output_tokens":260,"reasoning_output_tokens":172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5d594d9bb992005a9df593441e6eeb19b2fb91eb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"class\",\n    COUNT(*) AS support\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY support ASC, \"class\"\nLIMIT 12;","notes":"Uses the required Low-Support Group Count template with group_col bound to \"class\" on table \"n1\"."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18238bc3d7c3a054/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d510493f2561d8866f4cbcbd52a086059fcd164a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29909, "bytes_utf8": 29909, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 886, "bytes_utf8": 886, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20455, "cached_input_tokens": 12032, "output_tokens": 1297, "reasoning_output_tokens": 1034}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d457df37c443d14470c96e9e0635ecedbc4a91d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_182a46e6791e1b6a",
+    "api_calls": 0,
+    "input_tokens": 20455,
+    "cached_input_tokens": 12032,
+    "output_tokens": 1297,
+    "total_tokens": 21752,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 26213.37,
+    "sql_execution_elapsed_ms_total": 3.9,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..4078e2dfa810fd7ce011219ddd6054d2e11286e9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:49:33.759874+00:00",
+  "ended_at": "2026-05-19T15:49:59.973269+00:00",
+  "elapsed_ms": 26213.37,
+  "prompt_metrics": {
+    "chars": 29909,
+    "bytes_utf8": 29909,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1351,
+    "bytes_utf8": 1351,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 886,
+      "bytes_utf8": 886,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20455,
+      "cached_input_tokens": 12032,
+      "output_tokens": 1297,
+      "reasoning_output_tokens": 1034
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad220e0a37f3112ded40577e6bd18d49b95d41e2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_000.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_000",
+    "top_k": 14,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0941b3b87fc303f3d40a91967f22eb09339c299d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ed-9acd-7161-ba23-ce039e058e00"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS \\\"group_value\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_000\\\" IS NOT NULL\\n    AND TRIM(\\\"word_freq_000\\\") <> ''\\n  GROUP BY \\\"class\\\"\\n),\\n\\\"total\\\" AS (\\n  SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n  FROM \\\"grouped\\\"\\n)\\nSELECT\\n  \\\"g\\\".\\\"class\\\",\\n  \\\"g\\\".\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS \\\"g\\\"\\nCROSS JOIN \\\"total\\\" AS \\\"t\\\"\\nWHERE \\\"g\\\".\\\"group_value\\\" > \\\"t\\\".\\\"total_value\\\" * 0.1\\nORDER BY \\\"g\\\".\\\"group_value\\\" DESC;\",\"notes\":\"Applied the requested Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_000\\\". The measure is CAST to REAL because the schema stores numeric values as TEXT, and NULL/empty values are filtered before aggregation.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20455,"cached_input_tokens":12032,"output_tokens":1297,"reasoning_output_tokens":1034}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00f80edac22081e116302f8489e484992fcaf2c1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n  SELECT\n    \"class\",\n    SUM(CAST(\"word_freq_000\" AS REAL)) AS \"group_value\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_000\" IS NOT NULL\n    AND TRIM(\"word_freq_000\") <> ''\n  GROUP BY \"class\"\n),\n\"total\" AS (\n  SELECT SUM(\"group_value\") AS \"total_value\"\n  FROM \"grouped\"\n)\nSELECT\n  \"g\".\"class\",\n  \"g\".\"group_value\"\nFROM \"grouped\" AS \"g\"\nCROSS JOIN \"total\" AS \"t\"\nWHERE \"g\".\"group_value\" > \"t\".\"total_value\" * 0.1\nORDER BY \"g\".\"group_value\" DESC;","notes":"Applied the requested Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_000\". The measure is CAST to REAL because the schema stores numeric values as TEXT, and NULL/empty values are filtered before aggregation."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_182a46e6791e1b6a/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45f5a7300a7110b19e7ffd204aebbd60f07dfbc4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29912, "bytes_utf8": 29912, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 640, "bytes_utf8": 640, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20455, "cached_input_tokens": 19840, "output_tokens": 402, "reasoning_output_tokens": 231}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..50c0c10dc595d1ca2ab8c486ed6f3a0c51233cf2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_18f44851b6752dc7",
+    "api_calls": 0,
+    "input_tokens": 20455,
+    "cached_input_tokens": 19840,
+    "output_tokens": 402,
+    "total_tokens": 20857,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15734.69,
+    "sql_execution_elapsed_ms_total": 2.37,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9ec44e5195c7bb9f87ff4bcd416fb5b4d5ea078b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:48:41.347797+00:00",
+  "ended_at": "2026-05-19T15:48:57.082521+00:00",
+  "elapsed_ms": 15734.69,
+  "prompt_metrics": {
+    "chars": 29912,
+    "bytes_utf8": 29912,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1011,
+    "bytes_utf8": 1011,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 640,
+      "bytes_utf8": 640,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20455,
+      "cached_input_tokens": 19840,
+      "output_tokens": 402,
+      "reasoning_output_tokens": 231
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..55e7d84a00035feb58529671fe11f3392d55c1bb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_your.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_your",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 1.27,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6223f1cad188d2d79deaf1895d35ae20179c1a1b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ec-cdfe-7e21-b29f-cecd3970b23b"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_your\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_your\\\". Cast \\\"word_freq_your\\\" to REAL because the schema snapshot types it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20455,"cached_input_tokens":19840,"output_tokens":402,"reasoning_output_tokens":231}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1b5385deaf88751c15cc762bcdd4722199050a2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_your\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_your\". Cast \"word_freq_your\" to REAL because the schema snapshot types it as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..205856e5969548f1b5decb8cf65deeb6ac84f360
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_18f44851b6752dc7",
+  "api_calls": 0,
+  "input_tokens": 20455,
+  "cached_input_tokens": 19840,
+  "output_tokens": 402,
+  "total_tokens": 20857,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 15734.69,
+  "sql_execution_elapsed_ms_total": 2.37,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_18f44851b6752dc7/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90de6bdeb1b55828ff4910ad79f6dd2e28e08331
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 322, "bytes_utf8": 322, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20321, "cached_input_tokens": 12032, "output_tokens": 301, "reasoning_output_tokens": 213}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..223b1a65e5436975a4feaa10221ae1f186d87687
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_1af01943aea1ccef",
+    "api_calls": 0,
+    "input_tokens": 20321,
+    "cached_input_tokens": 12032,
+    "output_tokens": 301,
+    "total_tokens": 20622,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9942.01,
+    "sql_execution_elapsed_ms_total": 1.85,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9a3de218fdfa1f6e87936a3d3bf74921fecd444c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:03:15.135073+00:00",
+  "ended_at": "2026-05-19T16:03:25.077108+00:00",
+  "elapsed_ms": 9942.01,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 675,
+    "bytes_utf8": 675,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 322,
+      "bytes_utf8": 322,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20321,
+      "cached_input_tokens": 12032,
+      "output_tokens": 301,
+      "reasoning_output_tokens": 213
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1d90a0a5abb1e063557e2462242d5035249d82d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
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+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
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+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
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+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
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+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.8,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8e1ab240f05076398ed3840c2eed05d1ca7214ce
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-233c-7082-827e-f0b7a7d70ee4"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"class\\\",\\n    COUNT(*) AS support\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY support ASC, \\\"class\\\"\\nLIMIT 10;\",\"notes\":\"Applies the Low-Support Group Count template by grouping on \\\"class\\\" and returning class counts in ascending support order.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20321,"cached_input_tokens":12032,"output_tokens":301,"reasoning_output_tokens":213}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..295d605819b2f6908e59cad76a97b47c9126c95b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"class\",\n    COUNT(*) AS support\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY support ASC, \"class\"\nLIMIT 10;","notes":"Applies the Low-Support Group Count template by grouping on \"class\" and returning class counts in ascending support order."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1af01943aea1ccef/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..001496844445e040541ef4ea7c83faef593b0f62
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/run_manifest.json
@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:56:48.317650+00:00",
+  "ended_at": "2026-05-19T15:56:57.081258+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_1b29859e41bcb9ed",
+    "problem_id": "v2p_n1_72ada756bdb692b3",
+    "dataset_id": "n1",
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_concentration_consistency",
+    "intended_facet_id": "rare_target_concentration",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_85.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_85",
+      "top_k": 16,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "notes": [
+      "default_facets=rare_target_concentration",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=91"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..14c14477c95b491de053d52bac81cbae7c684232
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T15:56:51.604630+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3280.64, "started_at": "2026-05-19T15:56:48.321943+00:00", "ended_at": "2026-05-19T15:56:51.602620+00:00", "prompt_metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f4-3c5a-7ee3-9751-67f28d8b9574\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T15:56:57.081160+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 4472.58, "started_at": "2026-05-19T15:56:52.607576+00:00", "ended_at": "2026-05-19T15:56:57.080203+00:00", "prompt_metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f4-4d19-7462-a28d-23ff076da967\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..93bff1e3b1c529e3fd519ad311e72934b61ecec6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"word_freq_order": "0", "support": 3828, "avg_response": 0.07139759665621735}, {"word_freq_order": "0.09", "support": 26, "avg_response": 0.0}, {"word_freq_order": "0.08", "support": 24, "avg_response": 0.0}, {"word_freq_order": "0.8", "support": 18, "avg_response": 0.0}, {"word_freq_order": "0.1", "support": 17, "avg_response": 0.0}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..2e10d930259db8b80afff67a38c186067bc1fae3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n1_203b976aaa1605f1
+-- problem_id: v2p_n1_3f19512c62bcaa99
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "word_freq_order",
+    COUNT(*) AS support,
+    AVG("word_freq_3d") AS avg_response
+FROM "n1"
+GROUP BY "word_freq_order"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d952a5dee39c82af98a81c8414721b27fbe901d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n1_203b976aaa1605f1\n-- problem_id: v2p_n1_3f19512c62bcaa99\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"word_freq_order\",\n    COUNT(*) AS support,\n    AVG(\"word_freq_3d\") AS avg_response\nFROM \"n1\"\nGROUP BY \"word_freq_order\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n1_203b976aaa1605f1\\n-- problem_id: v2p_n1_3f19512c62bcaa99\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"word_freq_order\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"word_freq_3d\\\") AS avg_response\\nFROM \\\"n1\\\"\\nGROUP BY \\\"word_freq_order\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"word_freq_order\", \"support\", \"avg_response\"], \"rows\": [{\"word_freq_order\": \"0\", \"support\": 3828, \"avg_response\": 0.07139759665621735}, {\"word_freq_order\": \"0.09\", \"support\": 26, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.08\", \"support\": 24, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.8\", \"support\": 18, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.1\", \"support\": 17, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.16\", \"support\": 16, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.23\", \"support\": 16, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.05\", \"support\": 15, \"avg_response\": 0.02266666666666667}, {\"word_freq_order\": \"0.24\", \"support\": 15, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.06\", \"support\": 14, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.2\", \"support\": 14, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.32\", \"support\": 13, \"avg_response\": 0.01}, {\"word_freq_order\": \"0.4\", \"support\": 13, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.44\", \"support\": 13, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.54\", \"support\": 13, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.59\", \"support\": 13, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.27\", \"support\": 12, \"avg_response\": 0.7633333333333333}, {\"word_freq_order\": \"0.58\", \"support\": 12, \"avg_response\": 0.015833333333333335}, {\"word_freq_order\": \"0.28\", \"support\": 12, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.29\", \"support\": 12, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.25\", \"support\": 11, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.31\", \"support\": 11, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.66\", \"support\": 11, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.13\", \"support\": 10, \"avg_response\": 0.05500000000000001}, {\"word_freq_order\": \"0.37\", \"support\": 10, \"avg_response\": 0.006}, {\"word_freq_order\": \"0.35\", \"support\": 10, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.38\", \"support\": 10, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.48\", \"support\": 10, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.52\", \"support\": 10, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.57\", \"support\": 10, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.19\", \"support\": 9, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.34\", \"support\": 9, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.75\", \"support\": 9, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.42\", \"support\": 8, \"avg_response\": 0.23875}, {\"word_freq_order\": \"0.22\", \"support\": 8, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.87\", \"support\": 8, \"avg_response\": 0.0}, {\"word_freq_order\": \"1.15\", \"support\": 8, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.65\", \"support\": 7, \"avg_response\": 0.022857142857142857}, {\"word_freq_order\": \"0.07\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.15\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.17\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.36\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.55\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.6\", \"support\": 7, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.33\", \"support\": 6, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.5\", \"support\": 6, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.56\", \"support\": 6, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.61\", \"support\": 6, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.62\", \"support\": 6, \"avg_response\": 0.0}, {\"word_freq_order\": \"0.68\", \"support\": 6, \"avg_response\": 0.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.03}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..36450b82b92a3bb1be91dcbcb9535e9361096ebb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/run_manifest.json
@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.186154+00:00",
+  "ended_at": "2026-05-19T16:06:30.188894+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_203b976aaa1605f1",
+    "problem_id": "v2p_n1_3f19512c62bcaa99",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=word_freq_3d, key_col=word_freq_order.",
+    "bindings": {
+      "key_col": "word_freq_order",
+      "measure_col": "word_freq_3d",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_203b976aaa1605f1.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_203b976aaa1605f1/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..706198df1acf00a689c329062c2838dd721ccd88
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 463, "bytes_utf8": 463, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 19840, "output_tokens": 304, "reasoning_output_tokens": 179}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..30565d41f77133d48da9c8ca22d93af6a4350220
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_228f0ea024a4e354",
+    "api_calls": 0,
+    "input_tokens": 20373,
+    "cached_input_tokens": 19840,
+    "output_tokens": 304,
+    "total_tokens": 20677,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12231.42,
+    "sql_execution_elapsed_ms_total": 4.58,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..316a1d486088c65d53168e63d5dcf2d5801f4a83
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:58:28.158515+00:00",
+  "ended_at": "2026-05-19T15:58:31.448888+00:00",
+  "elapsed_ms": 3290.33,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29588,
+    "bytes_utf8": 29588,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5309a8740e4d12d99e6d70bdd63ade7d9c2bc641
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:58:32.450954+00:00",
+  "ended_at": "2026-05-19T15:58:41.392077+00:00",
+  "elapsed_ms": 8941.09,
+  "prompt_metrics": {
+    "chars": 29588,
+    "bytes_utf8": 29588,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 826,
+    "bytes_utf8": 826,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 463,
+      "bytes_utf8": 463,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20373,
+      "cached_input_tokens": 19840,
+      "output_tokens": 304,
+      "reasoning_output_tokens": 179
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1838a9555e1230e5de234bd5c8547165b172aad7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 13,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1838a9555e1230e5de234bd5c8547165b172aad7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 13,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..35eb590515b367a57990d2090643e1db8f42b823
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-c238-7b73-82b2-5e8dc592d89a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a742d31fbef2ce50476429138c1e65fe43b15289
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-c238-7b73-82b2-5e8dc592d89a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc10edc3f05653bec7752fad58f2ba4a9b907d36
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-d2f8-7df2-aec2-6d52c46d1bb9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Used the provided Grouped Condition Rate template with bound roles group_col=\\\"class\\\" and condition_col=\\\"class\\\" on single table \\\"n1\\\". Treated condition_value 0 as text ('0') because the schema snapshot shows \\\"class\\\" is stored as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20373,"cached_input_tokens":19840,"output_tokens":304,"reasoning_output_tokens":179}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d7e18be9723e64b1c84641951c36fb1f83d8ca68
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Used the provided Grouped Condition Rate template with bound roles group_col=\"class\" and condition_col=\"class\" on single table \"n1\". Treated condition_value 0 as text ('0') because the schema snapshot shows \"class\" is stored as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_228f0ea024a4e354/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c5f4ad3e292c6ee128f4b580ac512728410c3077
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29776, "bytes_utf8": 29776, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 647, "bytes_utf8": 647, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 12032, "output_tokens": 699, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..68b9010486c282440a4b44356d0956127a69f103
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2394ad0da6257203",
+    "api_calls": 0,
+    "input_tokens": 20438,
+    "cached_input_tokens": 12032,
+    "output_tokens": 699,
+    "total_tokens": 21137,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13527.97,
+    "sql_execution_elapsed_ms_total": 8.33,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f29c7811ad8ae8347fab22d7fd80efaaaf75607
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:36:59.977362+00:00",
+  "ended_at": "2026-05-19T15:37:13.505373+00:00",
+  "elapsed_ms": 13527.97,
+  "prompt_metrics": {
+    "chars": 29776,
+    "bytes_utf8": 29776,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1029,
+    "bytes_utf8": 1029,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 647,
+      "bytes_utf8": 647,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20438,
+      "cached_input_tokens": 12032,
+      "output_tokens": 699,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f94cfd545de5a108bea8ef0fa8c0889f10486366
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_labs.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_labs",
+    "item_col": "word_freq_telnet",
+    "top_k": 19,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..90061e79fa4e013d81fd22caea60d5ec64a5de51
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e2-1a69-7e71-9e44-eb2064da143c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_telnet\\\",\\n  SUM(CAST(\\\"word_freq_labs\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_labs\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_labs\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_telnet\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Instantiated the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_telnet\\\", and measure_col=\\\"word_freq_labs\\\". CAST to REAL is used because the schema stores these numeric-looking fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20438,"cached_input_tokens":12032,"output_tokens":699,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa01cf3c018b1ad0f03366c01fdeb47f8a4d314a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_telnet\",\n  SUM(CAST(\"word_freq_labs\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_labs\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_labs\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_telnet\"\nORDER BY share_within_group DESC;","notes":"Instantiated the provided Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_telnet\", and measure_col=\"word_freq_labs\". CAST to REAL is used because the schema stores these numeric-looking fields as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2394ad0da6257203/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c166f4bafe5d0a64595c777bf0e9d3e9628ae3fd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29355, "bytes_utf8": 29355, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 378, "bytes_utf8": 378, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 214, "reasoning_output_tokens": 115}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4d04e341b5a1baa733372ba2566b2e4502207492
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_258f2c51fdd91da8",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 214,
+    "total_tokens": 20529,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 7802.3,
+    "sql_execution_elapsed_ms_total": 2.26,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ba0a4374be528c16d34badca13478a8679363bfe
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:55.978476+00:00",
+  "ended_at": "2026-05-19T15:29:03.780806+00:00",
+  "elapsed_ms": 7802.3,
+  "prompt_metrics": {
+    "chars": 29355,
+    "bytes_utf8": 29355,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 732,
+    "bytes_utf8": 732,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 378,
+      "bytes_utf8": 378,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20315,
+      "cached_input_tokens": 12032,
+      "output_tokens": 214,
+      "reasoning_output_tokens": 115
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e2936b4ed5b8c617200889febbde8ea18e6385be
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_all.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_all",
+    "top_k": 12,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.42,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5b865893eb261ef22918eaa22a31df128dc38f57
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-b7b6-7b12-a1fe-74d7fc99dff2"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_all\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the provided Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_all\\\". CAST to REAL is used because the schema stores numeric-looking fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":214,"reasoning_output_tokens":115}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db3f103133d8f679bd015f6d8e2684b54252c3ab
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_all\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Applied the provided Grouped Numeric Sum template with group_col=\"class\" and measure_col=\"word_freq_all\". CAST to REAL is used because the schema stores numeric-looking fields as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ca19b4f1760b511bc66bb74cbce5c6008e23696
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_all.
+Result preview: [{"class": "1", "total_measure": 732.08}, {"class": "0", "total_measure": 559.22}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..ae576d1cdc75beff1e25d2e45359f4c1b95980f6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_258f2c51fdd91da8
+-- problem_id: v2p_n1_e00f455b5e47b808
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_all" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+ORDER BY total_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f789a88140e6a1ba5a2bc8b3e688b1f426a1940d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_all\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_all\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 732.08}, {\"class\": \"0\", \"total_measure\": 559.22}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.26}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e15f9287df392f07ef2d907c34ef2b687f863469
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:28:55.976065+00:00",
+  "ended_at": "2026-05-19T15:29:03.785100+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_258f2c51fdd91da8",
+    "problem_id": "v2p_n1_e00f455b5e47b808",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_conditional_contrast",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_all.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_all",
+      "top_k": 12,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.42,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=2"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_258f2c51fdd91da8.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_258f2c51fdd91da8",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 214,
+    "total_tokens": 20529,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 7802.3,
+    "sql_execution_elapsed_ms_total": 2.26,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c404cab2f087da81489735d513f0ea88bd099658
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:29:03.781599+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 7802.3, "started_at": "2026-05-19T15:28:55.978476+00:00", "ended_at": "2026-05-19T15:29:03.780806+00:00", "prompt_metrics": {"chars": 29355, "bytes_utf8": 29355, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 378, "bytes_utf8": 378, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 214, "reasoning_output_tokens": 115}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_all\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the provided Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_all\\\". CAST to REAL is used because the schema stores numeric-looking fields as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..83b67fdde5aa912350eb78e79b54c09f52ddaeec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_258f2c51fdd91da8",
+  "api_calls": 0,
+  "input_tokens": 20315,
+  "cached_input_tokens": 12032,
+  "output_tokens": 214,
+  "total_tokens": 20529,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 7802.3,
+  "sql_execution_elapsed_ms_total": 2.26,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_258f2c51fdd91da8/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc291ed5a600ea02f0f65175a2fe56871dbf5542
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29253, "bytes_utf8": 29253, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 296, "bytes_utf8": 296, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 12032, "output_tokens": 172, "reasoning_output_tokens": 97}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..57d4f8fc6687460769e782a10957f9e5cf1668f4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_280016155d6d3a6f",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 12032,
+    "output_tokens": 172,
+    "total_tokens": 20458,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18365.01,
+    "sql_execution_elapsed_ms_total": 4.12,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e662766dc6d28eb7369d382115ed4a149ab4dcc5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:32:44.137111+00:00",
+  "ended_at": "2026-05-19T15:33:02.502153+00:00",
+  "elapsed_ms": 18365.01,
+  "prompt_metrics": {
+    "chars": 29253,
+    "bytes_utf8": 29253,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1640,
+    "bytes_utf8": 1640,
+    "lines": 7,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 296,
+      "bytes_utf8": 296,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20286,
+      "cached_input_tokens": 12032,
+      "output_tokens": 172,
+      "reasoning_output_tokens": 97
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d17b071528efea31e1aa77e2168fdd02ea9e048f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.1,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..14a444f2513782c1d3442ccfb6e882d9a4b201a2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,7 @@
+{"type":"thread.started","thread_id":"019e40de-32f8-7362-b3c9-0b6d0c3fc343"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1987901, Requested 20921. Please try again in 264ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993069, Requested 20921. Please try again in 419ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS row_count\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the grouped count template with group_col bound to \\\"class\\\" to show the count distribution across the binary target categories.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":12032,"output_tokens":172,"reasoning_output_tokens":97}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..25876f1606968999cd310fa77dfda57a74b70f1d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS row_count\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY row_count DESC;","notes":"Uses the grouped count template with group_col bound to \"class\" to show the count distribution across the binary target categories."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_280016155d6d3a6f/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b25eaffb6488ae87bf477c02024ad81f8ad77fc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29541, "bytes_utf8": 29541, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 983, "bytes_utf8": 983, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 1677, "reasoning_output_tokens": 1394}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..2b820650fd388fdfcc8650a88d535b18b0630fc4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2855c5d450bd61f6",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 19840,
+    "output_tokens": 1677,
+    "total_tokens": 22032,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 26167.78,
+    "sql_execution_elapsed_ms_total": 23.28,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf3f633dc8b4f95e9278f6f6865f3484f3fb0a48
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:54:21.296543+00:00",
+  "ended_at": "2026-05-19T15:54:47.464353+00:00",
+  "elapsed_ms": 26167.78,
+  "prompt_metrics": {
+    "chars": 29541,
+    "bytes_utf8": 29541,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1431,
+    "bytes_utf8": 1431,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 983,
+      "bytes_utf8": 983,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 19840,
+      "output_tokens": 1677,
+      "reasoning_output_tokens": 1394
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1acd0fb05fb8f846d201ddd604d985125b6cb1c2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_telnet.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_telnet",
+    "top_k": 17,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db6a3dcc5e9de215050025e674d83fcf5b0323e2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f1-fe17-7331-b0ba-d9ae29d77cbe"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH ranked AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_telnet\\\" AS REAL) AS \\\"measure_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_telnet\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"class\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_telnet\\\" IS NOT NULL\\n), percentile_rows AS (\\n  SELECT\\n    \\\"class\\\",\\n    \\\"measure_value\\\",\\n    \\\"rn\\\",\\n    ((\\\"cnt\\\" * 95) + 99) / 100 AS \\\"target_rn\\\"\\n  FROM ranked\\n)\\nSELECT\\n  \\\"class\\\",\\n  \\\"measure_value\\\" AS \\\"percentile_measure\\\"\\nFROM percentile_rows\\nWHERE \\\"rn\\\" = \\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per class via window functions. \\\"word_freq_telnet\\\" is cast from TEXT to REAL because the schema stores numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":1677,"reasoning_output_tokens":1394}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..56e13fc368f03508646aa673e75aedb57c417f06
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH ranked AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_telnet\" AS REAL) AS \"measure_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_telnet\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"class\"\n    ) AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_telnet\" IS NOT NULL\n), percentile_rows AS (\n  SELECT\n    \"class\",\n    \"measure_value\",\n    \"rn\",\n    ((\"cnt\" * 95) + 99) / 100 AS \"target_rn\"\n  FROM ranked\n)\nSELECT\n  \"class\",\n  \"measure_value\" AS \"percentile_measure\"\nFROM percentile_rows\nWHERE \"rn\" = \"target_rn\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per class via window functions. \"word_freq_telnet\" is cast from TEXT to REAL because the schema stores numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2855c5d450bd61f6/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d10d6558dcb8b246e6cdacca8694152d6d0aa68
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29582, "bytes_utf8": 29582, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29582, "bytes_utf8": 29582, "lines": 795, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 480, "bytes_utf8": 480, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 19840, "output_tokens": 417, "reasoning_output_tokens": 286}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee156548a224c1594342ef36f637aa7b68606bb7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_290e42927cc03aa3",
+    "api_calls": 0,
+    "input_tokens": 20373,
+    "cached_input_tokens": 19840,
+    "output_tokens": 417,
+    "total_tokens": 20790,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15499.29,
+    "sql_execution_elapsed_ms_total": 3.45,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..da2bfda660f2561c1c33d1ae99e04b048e33da1e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:59:44.545173+00:00",
+  "ended_at": "2026-05-19T15:59:48.877590+00:00",
+  "elapsed_ms": 4332.39,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29582,
+    "bytes_utf8": 29582,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..cce009a77b44e5758945bebb974fa81753624366
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:59:49.881101+00:00",
+  "ended_at": "2026-05-19T16:00:01.048059+00:00",
+  "elapsed_ms": 11166.9,
+  "prompt_metrics": {
+    "chars": 29582,
+    "bytes_utf8": 29582,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 839,
+    "bytes_utf8": 839,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 480,
+      "bytes_utf8": 480,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20373,
+      "cached_input_tokens": 19840,
+      "output_tokens": 417,
+      "reasoning_output_tokens": 286
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..60e5f1f6e97c066e1870ed8b87594679fb8fdc78
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 16,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.11,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..60e5f1f6e97c066e1870ed8b87594679fb8fdc78
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 16,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.11,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78d22f2ae22062a03061ccb4ac8ec21c22835e17
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-eca7-7b12-8307-826d27649912"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5879ff48e885517caa9b98194fdcd680c5636a4e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-eca7-7b12-8307-826d27649912"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..177ed432f404b7caf8293466090132be1c5a855d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-0190-7912-a7e4-7bdfa9d17241"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the required Grouped Condition Rate template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Because the grouping column and condition column are the same, the resulting rate is 1.0 for the group where \\\"class\\\"='1' and 0.0 for other class groups.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20373,"cached_input_tokens":19840,"output_tokens":417,"reasoning_output_tokens":286}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95a2e6b5d936c74d7b2375b47159050aed1a8c6f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Uses the required Grouped Condition Rate template with group_col=\"class\" and condition_col=\"class\". Because the grouping column and condition column are the same, the resulting rate is 1.0 for the group where \"class\"='1' and 0.0 for other class groups."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_290e42927cc03aa3/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..175f966ba5ab7db7ee7ed7597b5551f754ded900
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29355, "bytes_utf8": 29355, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 373, "bytes_utf8": 373, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 268, "reasoning_output_tokens": 171}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..af281ac346d7ab32525bc20557773304071fd2c9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2a709975f1062895",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 268,
+    "total_tokens": 20583,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 17296.06,
+    "sql_execution_elapsed_ms_total": 3.5,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ea7ad393df4fd33b5ec0614e26432e72c0c0d5d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:30:12.203104+00:00",
+  "ended_at": "2026-05-19T15:30:29.499201+00:00",
+  "elapsed_ms": 17296.06,
+  "prompt_metrics": {
+    "chars": 29355,
+    "bytes_utf8": 29355,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1393,
+    "bytes_utf8": 1393,
+    "lines": 6,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 373,
+      "bytes_utf8": 373,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20315,
+      "cached_input_tokens": 12032,
+      "output_tokens": 268,
+      "reasoning_output_tokens": 171
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c9c35755c8069d74c0599a7ec4ef8073c0402959
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_over.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_over",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..07ce4202f5909819a6e10d92f0c1774b25ce8132
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,6 @@
+{"type":"thread.started","thread_id":"019e40db-e15d-7440-a1b9-f59269fbaa6c"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1998704, Requested 20950. Please try again in 589ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20950. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_over\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the required grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_over\\\". CAST to REAL is included because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":268,"reasoning_output_tokens":171}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e73cc791f04eb8fc05fccfa766fdbcf09aa200e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_over\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Used the required grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_over\". CAST to REAL is included because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2a709975f1062895/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05ad36bd8c9a18d654b244cb345f03cf1ed462ee
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_you.
+Result preview: [{"class": "1", "group_value": 4105.61}, {"class": "0", "group_value": 3541.71}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..a9ffc2112de283fcb8d3b2357defeda56212be08
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_2c2876d22a53423d
+-- problem_id: v2p_n1_fc6badc7006816fa
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_you" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f5c649be29c39773c73b4b3c11efa47ea72f002
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 4105.61}, {\"class\": \"0\", \"group_value\": 3541.71}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.45}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0054b2a34ade83166d17e320a8c4f0f8e7c3ced
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:47:58.486701+00:00",
+  "ended_at": "2026-05-19T15:48:14.604648+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_2c2876d22a53423d",
+    "problem_id": "v2p_n1_fc6badc7006816fa",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_you.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_you",
+      "top_k": 15,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 2.14,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=2/2",
+      "binding_index=75"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_2c2876d22a53423d.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2c2876d22a53423d",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 19840,
+    "output_tokens": 504,
+    "total_tokens": 20955,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 16107.47,
+    "sql_execution_elapsed_ms_total": 2.45,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..985e70922b990b9036ede364cd77466b5c8728c8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:48:14.600804+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16107.47, "started_at": "2026-05-19T15:47:58.492433+00:00", "ended_at": "2026-05-19T15:48:14.599956+00:00", "prompt_metrics": {"chars": 29911, "bytes_utf8": 29911, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 604, "bytes_utf8": 604, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 504, "reasoning_output_tokens": 344}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is used because the schema stores numeric-looking values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..79274a297426b6bd3625a790d4b69617c6966df6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_2c2876d22a53423d",
+  "api_calls": 0,
+  "input_tokens": 20451,
+  "cached_input_tokens": 19840,
+  "output_tokens": 504,
+  "total_tokens": 20955,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 16107.47,
+  "sql_execution_elapsed_ms_total": 2.45,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37608edfb91f87a6073e965821ee84e2c4c43f9a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29535, "bytes_utf8": 29535, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1708, "bytes_utf8": 1708, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 2517, "reasoning_output_tokens": 1965}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..27acbaec1dd161ca475a25642a0b96d1ae792c89
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2ca404d46c1016f4",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 19840,
+    "output_tokens": 2517,
+    "total_tokens": 22872,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 37577.22,
+    "sql_execution_elapsed_ms_total": 28.15,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..25741d523a10ac52184459ad7606d226f3049d5c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:52:31.009812+00:00",
+  "ended_at": "2026-05-19T15:53:08.587091+00:00",
+  "elapsed_ms": 37577.22,
+  "prompt_metrics": {
+    "chars": 29535,
+    "bytes_utf8": 29535,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2285,
+    "bytes_utf8": 2285,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1708,
+      "bytes_utf8": 1708,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 19840,
+      "output_tokens": 2517,
+      "reasoning_output_tokens": 1965
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c67c41e044db5080dd7215783a73b067bd2a5df6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_labs.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_labs",
+    "top_k": 11,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d16d3e8104ae3ea5d49b81bdd65131e8d8f27850
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f0-4f43-71f2-93a7-785976bb513f"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n  SELECT\\n    \\\"class\\\" AS \\\"group_value\\\",\\n    CAST(\\\"word_freq_labs\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_labs\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"class\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_labs\\\" IS NOT NULL\\n),\\n\\\"positioned\\\" AS (\\n  SELECT\\n    \\\"group_value\\\",\\n    \\\"measure\\\",\\n    \\\"rn\\\",\\n    \\\"cnt\\\",\\n    (1.0 + 0.95 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST((1.0 + 0.95 * (\\\"cnt\\\" - 1)) AS INTEGER) AS \\\"lower_rn\\\",\\n    CAST((1.0 + 0.95 * (\\\"cnt\\\" - 1)) AS INTEGER)\\n      + CASE\\n          WHEN (1.0 + 0.95 * (\\\"cnt\\\" - 1)) > CAST((1.0 + 0.95 * (\\\"cnt\\\" - 1)) AS INTEGER) THEN 1\\n          ELSE 0\\n        END AS \\\"upper_rn\\\"\\n  FROM \\\"ordered\\\"\\n)\\nSELECT\\n  \\\"group_value\\\" AS \\\"class\\\",\\n  CASE\\n    WHEN MAX(\\\"lower_rn\\\") = MAX(\\\"upper_rn\\\") THEN\\n      MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"measure\\\" END)\\n    ELSE\\n      MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"measure\\\" END)\\n      + (MAX(\\\"pos\\\") - MAX(\\\"lower_rn\\\")) * (\\n          MAX(CASE WHEN \\\"rn\\\" = \\\"upper_rn\\\" THEN \\\"measure\\\" END)\\n          - MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"measure\\\" END)\\n        )\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"positioned\\\"\\nGROUP BY \\\"group_value\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite lacks PERCENTILE_CONT, so this emulates the bound 0.95 continuous percentile per \\\"class\\\" using window ordering plus linear interpolation. \\\"word_freq_labs\\\" is CAST to REAL because the schema stores numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":2517,"reasoning_output_tokens":1965}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..32c2897c0cc9f17d67d6d10844006e0c3576a4da
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n  SELECT\n    \"class\" AS \"group_value\",\n    CAST(\"word_freq_labs\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_labs\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"class\"\n    ) AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_labs\" IS NOT NULL\n),\n\"positioned\" AS (\n  SELECT\n    \"group_value\",\n    \"measure\",\n    \"rn\",\n    \"cnt\",\n    (1.0 + 0.95 * (\"cnt\" - 1)) AS \"pos\",\n    CAST((1.0 + 0.95 * (\"cnt\" - 1)) AS INTEGER) AS \"lower_rn\",\n    CAST((1.0 + 0.95 * (\"cnt\" - 1)) AS INTEGER)\n      + CASE\n          WHEN (1.0 + 0.95 * (\"cnt\" - 1)) > CAST((1.0 + 0.95 * (\"cnt\" - 1)) AS INTEGER) THEN 1\n          ELSE 0\n        END AS \"upper_rn\"\n  FROM \"ordered\"\n)\nSELECT\n  \"group_value\" AS \"class\",\n  CASE\n    WHEN MAX(\"lower_rn\") = MAX(\"upper_rn\") THEN\n      MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"measure\" END)\n    ELSE\n      MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"measure\" END)\n      + (MAX(\"pos\") - MAX(\"lower_rn\")) * (\n          MAX(CASE WHEN \"rn\" = \"upper_rn\" THEN \"measure\" END)\n          - MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"measure\" END)\n        )\n  END AS \"percentile_measure\"\nFROM \"positioned\"\nGROUP BY \"group_value\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite lacks PERCENTILE_CONT, so this emulates the bound 0.95 continuous percentile per \"class\" using window ordering plus linear interpolation. \"word_freq_labs\" is CAST to REAL because the schema stores numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2ca404d46c1016f4/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d029c3cd281080e889a0f63f929c776015033951
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_internet.
+Result preview: [{"word_freq_internet": 11.11}, {"word_freq_internet": 6.06}, {"word_freq_internet": 5.88}, {"word_freq_internet": 4.68}, {"word_freq_internet": 4.62}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..d7c99ccb2bb2296bcb1f9ec5229225a1ad42799c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/generated_sql.sql
@@ -0,0 +1,22 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_m4_quantile_tail_slice
+-- query_record_id: v2q_n1_2d71c7b6d450c813
+-- problem_id: v2p_n1_c561bd662341d9de
+-- realization_mode: agent
+-- source_kind: agent
+WITH "buckets" AS (
+  SELECT CAST("word_freq_internet" AS REAL) AS "word_freq_internet",
+         NTILE(10) OVER (ORDER BY CAST("word_freq_internet" AS REAL) DESC) AS "tail_bucket"
+  FROM "n1"
+)
+SELECT "word_freq_internet"
+FROM "buckets"
+WHERE "tail_bucket" = 1
+ORDER BY "word_freq_internet" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c1dfdac3eac53a17f3a36267b03e00c74fd00c5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n  SELECT CAST(\"word_freq_internet\" AS REAL) AS \"word_freq_internet\",\n         NTILE(10) OVER (ORDER BY CAST(\"word_freq_internet\" AS REAL) DESC) AS \"tail_bucket\"\n  FROM \"n1\"\n)\nSELECT \"word_freq_internet\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_internet\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n  SELECT CAST(\\\"word_freq_internet\\\" AS REAL) AS \\\"word_freq_internet\\\",\\n         NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_internet\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n  FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_internet\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_internet\\\" DESC;\", \"columns\": [\"word_freq_internet\"], \"rows\": [{\"word_freq_internet\": 11.11}, {\"word_freq_internet\": 6.06}, {\"word_freq_internet\": 5.88}, {\"word_freq_internet\": 4.68}, {\"word_freq_internet\": 4.62}, {\"word_freq_internet\": 4.23}, {\"word_freq_internet\": 4.0}, {\"word_freq_internet\": 3.97}, {\"word_freq_internet\": 3.63}, {\"word_freq_internet\": 3.57}, {\"word_freq_internet\": 3.33}, {\"word_freq_internet\": 3.19}, {\"word_freq_internet\": 3.19}, {\"word_freq_internet\": 3.12}, {\"word_freq_internet\": 3.12}, {\"word_freq_internet\": 3.04}, {\"word_freq_internet\": 2.98}, {\"word_freq_internet\": 2.96}, {\"word_freq_internet\": 2.95}, {\"word_freq_internet\": 2.94}, {\"word_freq_internet\": 2.82}, {\"word_freq_internet\": 2.8}, {\"word_freq_internet\": 2.63}, {\"word_freq_internet\": 2.53}, {\"word_freq_internet\": 2.5}, {\"word_freq_internet\": 2.5}, {\"word_freq_internet\": 2.45}, {\"word_freq_internet\": 2.38}, {\"word_freq_internet\": 2.34}, {\"word_freq_internet\": 2.24}, {\"word_freq_internet\": 2.22}, {\"word_freq_internet\": 2.22}, {\"word_freq_internet\": 2.2}, {\"word_freq_internet\": 2.07}, {\"word_freq_internet\": 2.04}, {\"word_freq_internet\": 2.04}, {\"word_freq_internet\": 2.04}, {\"word_freq_internet\": 2.02}, {\"word_freq_internet\": 1.88}, {\"word_freq_internet\": 1.86}, {\"word_freq_internet\": 1.85}, {\"word_freq_internet\": 1.85}, {\"word_freq_internet\": 1.81}, {\"word_freq_internet\": 1.78}, {\"word_freq_internet\": 1.78}, {\"word_freq_internet\": 1.65}, {\"word_freq_internet\": 1.62}, {\"word_freq_internet\": 1.61}, {\"word_freq_internet\": 1.6}, {\"word_freq_internet\": 1.58}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 11.1}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..fcce178e64fb9d8a3bd98b3d0ca3abef8e8134d1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/run_manifest.json
@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:45:35.634798+00:00",
+  "ended_at": "2026-05-19T15:45:45.988332+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_2d71c7b6d450c813",
+    "problem_id": "v2p_n1_c561bd662341d9de",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_internet.",
+    "bindings": {
+      "measure_col": "word_freq_internet",
+      "top_k": 14,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/1",
+      "binding_index=64"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 6,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_2d71c7b6d450c813.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2d71c7b6d450c813",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 19840,
+    "output_tokens": 445,
+    "total_tokens": 20817,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10336.56,
+    "sql_execution_elapsed_ms_total": 11.1,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..690595a3faac27cece21fc2461eb8b466cbed956
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:45:45.975013+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10336.56, "started_at": "2026-05-19T15:45:35.637160+00:00", "ended_at": "2026-05-19T15:45:45.973749+00:00", "prompt_metrics": {"chars": 29541, "bytes_utf8": 29541, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 636, "bytes_utf8": 636, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 445, "reasoning_output_tokens": 270}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n  SELECT CAST(\\\"word_freq_internet\\\" AS REAL) AS \\\"word_freq_internet\\\",\\n         NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_internet\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n  FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_internet\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_internet\\\" DESC;\",\"notes\":\"Applies the Quantile Tail Slice template with measure_col bound to \\\"word_freq_internet\\\" and num_tiles fixed at 10. CAST to REAL is used because the schema stores feature values as TEXT, and numeric ordering is required for a correct tail slice.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed128b3df5166f62f96836e815d6b681d71a539b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_2d71c7b6d450c813",
+  "api_calls": 0,
+  "input_tokens": 20372,
+  "cached_input_tokens": 19840,
+  "output_tokens": 445,
+  "total_tokens": 20817,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10336.56,
+  "sql_execution_elapsed_ms_total": 11.1,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7b5063d2657d64befb4fb23f48a12cc989823b23
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 611, "bytes_utf8": 611, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 372, "reasoning_output_tokens": 211}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..11da0997df58cea815fd3e5be3ef384d1a167a43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2f1e4bc8e51e590b",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 12032,
+    "output_tokens": 372,
+    "total_tokens": 20823,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9302.78,
+    "sql_execution_elapsed_ms_total": 5.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0ccf96ccced839be8ed0fe7bddfb604338abfa93
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:46:21.085100+00:00",
+  "ended_at": "2026-05-19T15:46:30.387915+00:00",
+  "elapsed_ms": 9302.78,
+  "prompt_metrics": {
+    "chars": 29910,
+    "bytes_utf8": 29910,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 978,
+    "bytes_utf8": 978,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 611,
+      "bytes_utf8": 611,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20451,
+      "cached_input_tokens": 12032,
+      "output_tokens": 372,
+      "reasoning_output_tokens": 211
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5177d570486c1748d0b5dfdf1b4e52830aaa1bd1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
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+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
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+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
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+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_free.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_free",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.1,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc5a69e84174401f16bbda84cacb8361f0f3901d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ea-aa9d-75e3-88e1-405c98dcec09"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_free\\\". CAST to REAL is included because the schema snapshot types this measure column as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":12032,"output_tokens":372,"reasoning_output_tokens":211}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c519b7f6c56046f84b4d0b77dd4eac6de127a3c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_free\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Used the required template with group_col=\"class\" and measure_col=\"word_freq_free\". CAST to REAL is included because the schema snapshot types this measure column as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..700b4be756db33bae9f778be8ac994c5fc1a3628
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_free.
+Result preview: [{"class": "1", "group_value": 939.79}, {"class": "0", "group_value": 205.16}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..75ce6066614d2858efb52fd74387421bcbb73d3e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_2f1e4bc8e51e590b
+-- problem_id: v2p_n1_7fa3c81d00508f40
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_free" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.1
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca87278b541ad18cee537c1e7f7c2dc2c338bcd3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_free\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 939.79}, {\"class\": \"0\", \"group_value\": 205.16}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.32}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..d8043ca8516a7127a7598cfd1db0332ef027c728
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:46:21.082386+00:00",
+  "ended_at": "2026-05-19T15:46:30.397357+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_2f1e4bc8e51e590b",
+    "problem_id": "v2p_n1_7fa3c81d00508f40",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_free.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_free",
+      "top_k": 12,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.1,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=72"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_2f1e4bc8e51e590b.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2f1e4bc8e51e590b",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 12032,
+    "output_tokens": 372,
+    "total_tokens": 20823,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9302.78,
+    "sql_execution_elapsed_ms_total": 5.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba30992e2f789bb596d24c990b31869eaec69ec7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:46:30.389042+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9302.78, "started_at": "2026-05-19T15:46:21.085100+00:00", "ended_at": "2026-05-19T15:46:30.387915+00:00", "prompt_metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 611, "bytes_utf8": 611, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 372, "reasoning_output_tokens": 211}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_free\\\". CAST to REAL is included because the schema snapshot types this measure column as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..11e5a02c0b51dee82dc4f9baf6354ffda6709ca2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_2f1e4bc8e51e590b",
+  "api_calls": 0,
+  "input_tokens": 20451,
+  "cached_input_tokens": 12032,
+  "output_tokens": 372,
+  "total_tokens": 20823,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 9302.78,
+  "sql_execution_elapsed_ms_total": 5.32,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f1e4bc8e51e590b/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..82afef62b332a97b69a188073db0200fd3163171
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:57:40.865551+00:00",
+  "ended_at": "2026-05-19T15:57:45.166110+00:00",
+  "elapsed_ms": 4300.53,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29547,
+    "bytes_utf8": 29547,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..c689ba02e2be5120ec200c1854fbadc227dfa507
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:57:46.169140+00:00",
+  "ended_at": "2026-05-19T15:57:49.310455+00:00",
+  "elapsed_ms": 3141.27,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29547,
+    "bytes_utf8": 29547,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..320e7a647766cc60dfa8f39a9c943e6963817ddf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_technology.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_technology",
+    "top_k": 17,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..320e7a647766cc60dfa8f39a9c943e6963817ddf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_technology.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_technology",
+    "top_k": 17,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c29090a1ca52a616c73ea53538ad47bf9677777c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-099d-7a40-b50e-96eda8204056"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8042bfa926ea9741d94fb444e49d1c99f3417203
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-099d-7a40-b50e-96eda8204056"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc6eb608bb104b26f4674fd8666e5c2247af9b42
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-1e49-78f1-86ee-9e556317b007"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6356f82ecbaca0b2709f6dadfabb7114b5e45ffc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-1e49-78f1-86ee-9e556317b007"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2f5ef9a992ccf1c9/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26aae4a6d6ad9d9222f525af872955e956ea020d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 639, "bytes_utf8": 639, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 423, "reasoning_output_tokens": 257}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c543cb09cc8ca5d3370fede003fa4a2b525bd53
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_2fdad4f03a31aa08",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 423,
+    "total_tokens": 20876,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9842.54,
+    "sql_execution_elapsed_ms_total": 6.38,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef01e97012199603ca9f18d417f08ba0e0ab1471
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:47:48.630609+00:00",
+  "ended_at": "2026-05-19T15:47:58.473203+00:00",
+  "elapsed_ms": 9842.54,
+  "prompt_metrics": {
+    "chars": 29910,
+    "bytes_utf8": 29910,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1006,
+    "bytes_utf8": 1006,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 639,
+      "bytes_utf8": 639,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20453,
+      "cached_input_tokens": 19840,
+      "output_tokens": 423,
+      "reasoning_output_tokens": 257
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b63c070106525fd4d28751c91217fa1c0de1dbc1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
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+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_you.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_you",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 2.64,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..013b6d0c6a6414715a13fcbd995508eb540036af
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ec-009b-7ee3-9253-56c5107990fe"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is used because the schema stores numeric-looking values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":423,"reasoning_output_tokens":257}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e73cb0f389a53b7d1828c9a6252750e912cd7e99
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_you\". CAST to REAL is used because the schema stores numeric-looking values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2fdad4f03a31aa08/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6c8148258086bb4f670ad5400e434df985f4e00
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30143, "bytes_utf8": 30143, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 734, "bytes_utf8": 734, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 662, "reasoning_output_tokens": 466}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..abd478f80abc9db9ce24df81a021ce0e5ef478ba
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_30aef82f1704a76f",
+    "api_calls": 0,
+    "input_tokens": 20520,
+    "cached_input_tokens": 19840,
+    "output_tokens": 662,
+    "total_tokens": 21182,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15397.01,
+    "sql_execution_elapsed_ms_total": 3.3,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1dff5fb52a603585a540ac4e3ef26041cc59cf4b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:39:34.108118+00:00",
+  "ended_at": "2026-05-19T15:39:49.505174+00:00",
+  "elapsed_ms": 15397.01,
+  "prompt_metrics": {
+    "chars": 30143,
+    "bytes_utf8": 30143,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1103,
+    "bytes_utf8": 1103,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 734,
+      "bytes_utf8": 734,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20520,
+      "cached_input_tokens": 19840,
+      "output_tokens": 662,
+      "reasoning_output_tokens": 466
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..74805027f7389a3e2cf30cd4d4fcc66e8ad175ca
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_ratio_two_conditions",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..56630f0905ebe4cda36d807dd7cd51f3161445a5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e4-7469-7980-95a1-3f9fee9b3ab8"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"class\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the required template with both group_col and condition_col bound to \\\"class\\\". Since \\\"class\\\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'. NULLIF protects against division by zero for the class='0' group.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":19840,"output_tokens":662,"reasoning_output_tokens":466}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..606c59d7de0b10e6a231d04901b81d1bbc47a9bb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n    SELECT \"class\",\n           SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT \"class\",\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Applied the required template with both group_col and condition_col bound to \"class\". Since \"class\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'. NULLIF protects against division by zero for the class='0' group."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30aef82f1704a76f/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..919eba6dd37c2303945d6cb2a817afdacc5ce3c2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29538, "bytes_utf8": 29538, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 576, "bytes_utf8": 576, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20370, "cached_input_tokens": 19840, "output_tokens": 411, "reasoning_output_tokens": 261}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1efd9f27ed6bdace85a7f7f4a23298d9ff0c8aa8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_30f3d8da7bd9b8d6",
+    "api_calls": 0,
+    "input_tokens": 20370,
+    "cached_input_tokens": 19840,
+    "output_tokens": 411,
+    "total_tokens": 20781,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12296.92,
+    "sql_execution_elapsed_ms_total": 7.14,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..50caada37877b093eba57a1397f84126a337868b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:46:08.774379+00:00",
+  "ended_at": "2026-05-19T15:46:21.071335+00:00",
+  "elapsed_ms": 12296.92,
+  "prompt_metrics": {
+    "chars": 29538,
+    "bytes_utf8": 29538,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 947,
+    "bytes_utf8": 947,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 576,
+      "bytes_utf8": 576,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20370,
+      "cached_input_tokens": 19840,
+      "output_tokens": 411,
+      "reasoning_output_tokens": 261
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..239aad1070d3638ebe04dde6d59295a01371813e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_receive.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "word_freq_receive",
+    "top_k": 12,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..391d0deaf2bf5bfa48a1a8b491996e28b983f42b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ea-7a24-7e62-877c-93c9eb09d9dd"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n    SELECT CAST(\\\"word_freq_receive\\\" AS REAL) AS \\\"word_freq_receive\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_receive\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n    FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_receive\\\"\\nFROM buckets\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_receive\\\" DESC;\",\"notes\":\"Applied the provided Quantile Tail Slice template with measure_col bound to \\\"word_freq_receive\\\" and num_tiles=10. Cast to REAL because the schema snapshot stores this numeric feature as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20370,"cached_input_tokens":19840,"output_tokens":411,"reasoning_output_tokens":261}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d960edbe46b7fd00f83eb9b3d1fd46076b50ef39
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n    SELECT CAST(\"word_freq_receive\" AS REAL) AS \"word_freq_receive\",\n           NTILE(10) OVER (ORDER BY CAST(\"word_freq_receive\" AS REAL) DESC) AS \"tail_bucket\"\n    FROM \"n1\"\n)\nSELECT \"word_freq_receive\"\nFROM buckets\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_receive\" DESC;","notes":"Applied the provided Quantile Tail Slice template with measure_col bound to \"word_freq_receive\" and num_tiles=10. Cast to REAL because the schema snapshot stores this numeric feature as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_30f3d8da7bd9b8d6/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a4878ef3b6d568f1995424e8305b69d36657786
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29767, "bytes_utf8": 29767, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 641, "bytes_utf8": 641, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20435, "cached_input_tokens": 12032, "output_tokens": 1986, "reasoning_output_tokens": 1803}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ea9c0bfd41858ba9b50912e50477ffac95b529e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_34452f808979cfac",
+    "api_calls": 0,
+    "input_tokens": 20435,
+    "cached_input_tokens": 12032,
+    "output_tokens": 1986,
+    "total_tokens": 22421,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 35525.21,
+    "sql_execution_elapsed_ms_total": 4.11,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec037d4b9e9bfc5504a0c0d59523ee2e787b7acc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:36:11.046302+00:00",
+  "ended_at": "2026-05-19T15:36:46.571544+00:00",
+  "elapsed_ms": 35525.21,
+  "prompt_metrics": {
+    "chars": 29767,
+    "bytes_utf8": 29767,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1037,
+    "bytes_utf8": 1037,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 641,
+      "bytes_utf8": 641,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20435,
+      "cached_input_tokens": 12032,
+      "output_tokens": 1986,
+      "reasoning_output_tokens": 1803
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ecef69c6dc4d7c86ab03452cc6f9b5339847f32
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_lab.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_lab",
+    "item_col": "word_freq_labs",
+    "top_k": 18,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..119714bda626b257e6265c333b26ed5bcf4f790e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e1-5b3c-7722-b599-a8f204fe2d51"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_labs\\\",\\n  SUM(CAST(\\\"word_freq_lab\\\" AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(\\\"word_freq_lab\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_lab\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_labs\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with bound roles group_col=\\\"class\\\", item_col=\\\"word_freq_labs\\\", and measure_col=\\\"word_freq_lab\\\". CAST to REAL is needed because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20435,"cached_input_tokens":12032,"output_tokens":1986,"reasoning_output_tokens":1803}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c044cef2da9c5ba827c97a9cb1d3c17960bb6061
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_labs\",\n  SUM(CAST(\"word_freq_lab\" AS REAL)) AS \"total_measure\",\n  SUM(CAST(\"word_freq_lab\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_lab\" AS REAL))) OVER (PARTITION BY \"class\") AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_labs\"\nORDER BY \"share_within_group\" DESC;","notes":"Used the provided Within-Group Share of Total template with bound roles group_col=\"class\", item_col=\"word_freq_labs\", and measure_col=\"word_freq_lab\". CAST to REAL is needed because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_34452f808979cfac/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..43fe2a5e9babd3abb913cf9281b230340a860102
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1023, "bytes_utf8": 1023, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 12032, "output_tokens": 1118, "reasoning_output_tokens": 802}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..f290ee1cfd378c1c83de5c5149705140bd53df61
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_35b1e70d0dfdcaca",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 12032,
+    "output_tokens": 1118,
+    "total_tokens": 21473,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 22504.79,
+    "sql_execution_elapsed_ms_total": 13.5,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..21799235527288b6ab6f16b234a3651d18754af1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:50:52.987833+00:00",
+  "ended_at": "2026-05-19T15:51:15.492659+00:00",
+  "elapsed_ms": 22504.79,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1502,
+    "bytes_utf8": 1502,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1023,
+      "bytes_utf8": 1023,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 12032,
+      "output_tokens": 1118,
+      "reasoning_output_tokens": 802
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c321c9a201b95a20494b52f2d8c9259be6b45f4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_650.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_650",
+    "top_k": 14,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ef45e95a461bf8d9bd7d040ca95970822ef8977
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ee-d047-7951-aa05-cb3456f884d9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(NULLIF(\\\"word_freq_650\\\", '') AS REAL) AS \\\"measure_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(NULLIF(\\\"word_freq_650\\\", '') AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"class\\\") AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE NULLIF(\\\"word_freq_650\\\", '') IS NOT NULL\\n),\\n\\\"cutoff\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    MIN(\\\"rn\\\") AS \\\"target_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"rn\\\" >= 0.95 * \\\"cnt\\\"\\n  GROUP BY \\\"class\\\"\\n)\\nSELECT\\n  r.\\\"class\\\",\\n  r.\\\"measure_value\\\" AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\" AS r\\nJOIN \\\"cutoff\\\" AS c\\n  ON r.\\\"class\\\" = c.\\\"class\\\"\\n AND r.\\\"rn\\\" = c.\\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per \\\"class\\\" via window functions. \\\"word_freq_650\\\" is CAST to REAL because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":12032,"output_tokens":1118,"reasoning_output_tokens":802}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0521db6c96dec430421c0be887743169fcc94ec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(NULLIF(\"word_freq_650\", '') AS REAL) AS \"measure_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(NULLIF(\"word_freq_650\", '') AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"class\") AS \"cnt\"\n  FROM \"n1\"\n  WHERE NULLIF(\"word_freq_650\", '') IS NOT NULL\n),\n\"cutoff\" AS (\n  SELECT\n    \"class\",\n    MIN(\"rn\") AS \"target_rn\"\n  FROM \"ranked\"\n  WHERE \"rn\" >= 0.95 * \"cnt\"\n  GROUP BY \"class\"\n)\nSELECT\n  r.\"class\",\n  r.\"measure_value\" AS \"percentile_measure\"\nFROM \"ranked\" AS r\nJOIN \"cutoff\" AS c\n  ON r.\"class\" = c.\"class\"\n AND r.\"rn\" = c.\"target_rn\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per \"class\" via window functions. \"word_freq_650\" is CAST to REAL because the schema stores feature columns as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_35b1e70d0dfdcaca/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e17aeb73c975ac612e6a293aa69bb80e960935c4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_remove.
+Result preview: [{"class": "1", "total_measure": 499.31}, {"class": "0", "total_measure": 26.16}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..6a4c2577dad1fde7ad31e2a3fec85811c35f257a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_rank_order
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_36c4dfa9539ee7b0
+-- problem_id: v2p_n1_7bb2d3cc27803287
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_remove" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+ORDER BY total_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afcad977ceb8dd42775ed0c43cff64e97382440e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_remove\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_remove\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 499.31}, {\"class\": \"0\", \"total_measure\": 26.16}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.92}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..3798596ce10b50772f29a43cc957d7d6ce95f187
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:30:58.413999+00:00",
+  "ended_at": "2026-05-19T15:31:40.992226+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_36c4dfa9539ee7b0",
+    "problem_id": "v2p_n1_7bb2d3cc27803287",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_rank_order",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_remove.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_remove",
+      "top_k": 16,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=6"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_36c4dfa9539ee7b0.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_36c4dfa9539ee7b0",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 19840,
+    "output_tokens": 337,
+    "total_tokens": 20652,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 42566.67,
+    "sql_execution_elapsed_ms_total": 4.92,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..660bf69bad77cc546c507649b390faad852818ab
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:31:40.984575+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 42566.67, "started_at": "2026-05-19T15:30:58.416001+00:00", "ended_at": "2026-05-19T15:31:40.982707+00:00", "prompt_metrics": {"chars": 29361, "bytes_utf8": 29361, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 378, "bytes_utf8": 378, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 19840, "output_tokens": 337, "reasoning_output_tokens": 239}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_remove\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Uses the requested Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_remove\\\". CAST to REAL is included because the schema stores feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..cfce9c1b082f42b09561a3beafcea0a6cb3acb09
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_36c4dfa9539ee7b0",
+  "api_calls": 0,
+  "input_tokens": 20315,
+  "cached_input_tokens": 19840,
+  "output_tokens": 337,
+  "total_tokens": 20652,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 42566.67,
+  "sql_execution_elapsed_ms_total": 4.92,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f938ab5b8db382b56d1c62aab5a3f9d5d1cac36d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 350, "bytes_utf8": 350, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20322, "cached_input_tokens": 12032, "output_tokens": 223, "reasoning_output_tokens": 126}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b780c6e9f877e191e9b77d6912da97b857f83e8a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_3817c21ee441b659",
+    "api_calls": 0,
+    "input_tokens": 20322,
+    "cached_input_tokens": 12032,
+    "output_tokens": 223,
+    "total_tokens": 20545,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 7860.3,
+    "sql_execution_elapsed_ms_total": 2.42,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d5f03a892e3d907c066be23ec1de80db7c81bf7a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:47.259168+00:00",
+  "ended_at": "2026-05-19T16:01:55.119495+00:00",
+  "elapsed_ms": 7860.3,
+  "prompt_metrics": {
+    "chars": 29335,
+    "bytes_utf8": 29335,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 703,
+    "bytes_utf8": 703,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 350,
+      "bytes_utf8": 350,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20322,
+      "cached_input_tokens": 12032,
+      "output_tokens": 223,
+      "reasoning_output_tokens": 126
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..647f0bd7f4c5c8c008cc75ead619a90d4f4d47d4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 15,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8a8b3779589743e5b9d3b544e69d15147d041878
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-cc1b-7c31-8c18-2918edf2b18c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"class\\\",\\n    COUNT(*) AS support\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY support ASC, \\\"class\\\"\\nLIMIT 15;\",\"notes\":\"Applies the planned Low-Support Group Count template with group_col bound to \\\"class\\\" and top_k set to 15 to inspect the class count distribution tail.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20322,"cached_input_tokens":12032,"output_tokens":223,"reasoning_output_tokens":126}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..86c9cab305ffb694102e898314d63c02a7f4ebe2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"class\",\n    COUNT(*) AS support\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY support ASC, \"class\"\nLIMIT 15;","notes":"Applies the planned Low-Support Group Count template with group_col bound to \"class\" and top_k set to 15 to inspect the class count distribution tail."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3817c21ee441b659/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95d34b2318a4e40ce7dd942e32aebf1bac69d09a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29771, "bytes_utf8": 29771, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 635, "bytes_utf8": 635, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 12032, "output_tokens": 695, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..14556848dfc39335ebe6f513ba198f37f50f09d9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_38c5dcb4bebd5fa9",
+    "api_calls": 0,
+    "input_tokens": 20436,
+    "cached_input_tokens": 12032,
+    "output_tokens": 695,
+    "total_tokens": 21131,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15340.6,
+    "sql_execution_elapsed_ms_total": 6.7,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1333ddef2cebd41f2a6f293ce3922cacbee86dc0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:38:22.764307+00:00",
+  "ended_at": "2026-05-19T15:38:38.104945+00:00",
+  "elapsed_ms": 15340.6,
+  "prompt_metrics": {
+    "chars": 29771,
+    "bytes_utf8": 29771,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1017,
+    "bytes_utf8": 1017,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 635,
+      "bytes_utf8": 635,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20436,
+      "cached_input_tokens": 12032,
+      "output_tokens": 695,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1dd74f4b8a22245ecf3878052fc2b841a93c858e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_data.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_data",
+    "item_col": "word_freq_415",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a3a8c62fdc3a9ace727602a2063e4f507448b7e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e3-5da7-7c61-b083-42af27928665"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_415\\\",\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_data\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_415\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_415\\\", and measure_col=\\\"word_freq_data\\\". CAST to REAL is used because the schema stores these numeric fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20436,"cached_input_tokens":12032,"output_tokens":695,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4d5c2a3cfd1642c48869cd318b9d727199e696ce
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_415\",\n  SUM(CAST(\"word_freq_data\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_data\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_data\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_415\"\nORDER BY share_within_group DESC;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_415\", and measure_col=\"word_freq_data\". CAST to REAL is used because the schema stores these numeric fields as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_38c5dcb4bebd5fa9/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..201f14e8b8710c92444a48cbad1b7904d6e8c8ed
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29575, "bytes_utf8": 29575, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 263, "reasoning_output_tokens": 152}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e1b4eb8ae86aa594fad60da2857619d2f37ac44f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_3bb7c90ac0f0a63b",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 19840,
+    "output_tokens": 263,
+    "total_tokens": 20635,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8178.87,
+    "sql_execution_elapsed_ms_total": 2.81,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..64750e6df7c0c0daea63404164e4c964ecc272f6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:59:36.356761+00:00",
+  "ended_at": "2026-05-19T15:59:44.535664+00:00",
+  "elapsed_ms": 8178.87,
+  "prompt_metrics": {
+    "chars": 29575,
+    "bytes_utf8": 29575,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 775,
+    "bytes_utf8": 775,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 416,
+      "bytes_utf8": 416,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20372,
+      "cached_input_tokens": 19840,
+      "output_tokens": 263,
+      "reasoning_output_tokens": 152
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..29e5cc8d2a573211bc69d23592f995195379e2c4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 11,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.11,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..173625a76257f1bcf83b6a455f9d1304d9f233e9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-ccab-7df0-900c-85456aa15cf7"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate.\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Compared against text literal '0' because the schema snapshot shows \\\"class\\\" as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":263,"reasoning_output_tokens":152}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..36dd66c1f497137ae2cf30d5827a1e2c1546afdc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate.\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Applied the provided Grouped Condition Rate template with group_col=\"class\" and condition_col=\"class\". Compared against text literal '0' because the schema snapshot shows \"class\" as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3bb7c90ac0f0a63b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..580f8f6d9c71c274137200032e5bb722ad08407d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.
+Result preview: [{"class": "0", "row_count": 2788}, {"class": "1", "row_count": 1813}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..21deac129e9b20e2da645c318d46a1b01656496d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: subgroup_size_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_clickbench_group_count
+-- query_record_id: v2q_n1_3be9acbd219c318a
+-- problem_id: v2p_n1_70e18fec62aa877e
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", COUNT(*) AS "row_count"
+FROM "n1"
+GROUP BY "class"
+ORDER BY "row_count" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ccefa9eb9013f90f538adfff238f94f0a3190dd6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"class\", \"row_count\"], \"rows\": [{\"class\": \"0\", \"row_count\": 2788}, {\"class\": \"1\", \"row_count\": 1813}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.89}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..b7984b16d5bc272101c2fef40b59e4532cef54c3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/run_manifest.json
@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:33:37.714517+00:00",
+  "ended_at": "2026-05-19T15:33:57.501833+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_3be9acbd219c318a",
+    "problem_id": "v2p_n1_70e18fec62aa877e",
+    "dataset_id": "n1",
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "subgroup_size_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "top_k": 13,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 2.64,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=1/1",
+      "binding_index=18"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 2,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_3be9acbd219c318a.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_3be9acbd219c318a",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 19840,
+    "output_tokens": 255,
+    "total_tokens": 20541,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 19776.31,
+    "sql_execution_elapsed_ms_total": 3.89,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2d44154d9ee4d80d6827e09ca2394caf1cf43b7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:33:57.495228+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 19776.31, "started_at": "2026-05-19T15:33:37.717087+00:00", "ended_at": "2026-05-19T15:33:57.493436+00:00", "prompt_metrics": {"chars": 29255, "bytes_utf8": 29255, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 268, "bytes_utf8": 268, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 255, "reasoning_output_tokens": 181}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped-count template with group_col fixed to \\\"class\\\" to show the class distribution.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f34c28478a8d18bcd0ff0bfbc7a134b460581fc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_3be9acbd219c318a",
+  "api_calls": 0,
+  "input_tokens": 20286,
+  "cached_input_tokens": 19840,
+  "output_tokens": 255,
+  "total_tokens": 20541,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 19776.31,
+  "sql_execution_elapsed_ms_total": 3.89,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06ac2ff9e555a1844bfd1f3f69344ad22b279d5b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 450, "bytes_utf8": 450, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 426, "reasoning_output_tokens": 303}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d6520a2ae5e04b05b0d4de473de0b3fd1bb0616
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_3fc9c9b59a7ee71f",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 19840,
+    "output_tokens": 426,
+    "total_tokens": 20798,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14350.74,
+    "sql_execution_elapsed_ms_total": 4.56,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2bdca2c8bf3099b1861dc7130e40acac016c0b11
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:58:41.406765+00:00",
+  "ended_at": "2026-05-19T15:58:55.757550+00:00",
+  "elapsed_ms": 14350.74,
+  "prompt_metrics": {
+    "chars": 29585,
+    "bytes_utf8": 29585,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 809,
+    "bytes_utf8": 809,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 450,
+      "bytes_utf8": 450,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20372,
+      "cached_input_tokens": 19840,
+      "output_tokens": 426,
+      "reasoning_output_tokens": 303
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bbc87bef56ae26d5ebea3695f372c7e064270e87
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
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+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
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+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
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+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
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+      "word_freq_receive": "0.31",
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+      "word_freq_people": "0.31",
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+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
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+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
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+      "word_freq_project": "0",
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+      "char_freq_%3B": "0",
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+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 18,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3dff039f876bb00ba625a2bcd7ec604e06d1e8c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-f611-7c51-86da-9bb605762c84"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Applied the grouped condition rate template exactly with both group_col and condition_col bound to \\\"class\\\"; this measures the within-class rate of class='1', which is inherently 1 for spam rows and 0 for non-spam rows.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":426,"reasoning_output_tokens":303}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..24d98fee0e81f59dfc87716f57d0d30afff0ecaa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"condition_rate\" DESC;","notes":"Applied the grouped condition rate template exactly with both group_col and condition_col bound to \"class\"; this measures the within-class rate of class='1', which is inherently 1 for spam rows and 0 for non-spam rows."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fc9c9b59a7ee71f/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00069bc1bdb3008e93e0c8ef159e3b3b5eee2a91
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.
+Result preview: [{"class": "1", "condition_ratio": 0.0}, {"class": "0", "condition_ratio": null}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..f0e960f3dc47062ae831d9104dd7caf18bc89e5d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: contrastive_conditional_view
+-- template_id: tpl_m4_group_ratio_two_conditions
+-- query_record_id: v2q_n1_4069c2f7e4b1c80d
+-- problem_id: v2p_n1_297dddb9c4f9e8a1
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+    SELECT
+        "class",
+        SUM(CASE WHEN "class" = '0' THEN 1 ELSE 0 END) AS "numerator_count",
+        SUM(CASE WHEN "class" = '1' THEN 1 ELSE 0 END) AS "denominator_count"
+    FROM "n1"
+    GROUP BY "class"
+)
+SELECT
+    "class",
+    CAST("numerator_count" AS FLOAT) / NULLIF("denominator_count", 0) AS "condition_ratio"
+FROM "grouped"
+ORDER BY "condition_ratio" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..baed7a9045877d721dfd61e135e5bdda13123abd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n    SELECT\n        \"class\",\n        SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS \"numerator_count\",\n        SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS \"denominator_count\"\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT\n    \"class\",\n    CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n    SELECT\\n        \\\"class\\\",\\n        SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n        SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT\\n    \\\"class\\\",\\n    CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\", \"columns\": [\"class\", \"condition_ratio\"], \"rows\": [{\"class\": \"1\", \"condition_ratio\": 0.0}, {\"class\": \"0\", \"condition_ratio\": null}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.18}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..feb355fba802fb9ff90a0b776876052aedb427b5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/run_manifest.json
@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:40:29.452745+00:00",
+  "ended_at": "2026-05-19T15:40:49.553310+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_4069c2f7e4b1c80d",
+    "problem_id": "v2p_n1_297dddb9c4f9e8a1",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "contrastive_conditional_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "condition_col": "class",
+      "condition_value": "0",
+      "positive_value": "0",
+      "negative_value": "1",
+      "top_k": 11,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/1",
+      "binding_index=41"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 4,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_4069c2f7e4b1c80d.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_4069c2f7e4b1c80d",
+    "api_calls": 0,
+    "input_tokens": 20520,
+    "cached_input_tokens": 19840,
+    "output_tokens": 473,
+    "total_tokens": 20993,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 20085.87,
+    "sql_execution_elapsed_ms_total": 5.18,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d5d80715f50e690771075ff64e5facde606723b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:40:49.544954+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 20085.87, "started_at": "2026-05-19T15:40:29.457232+00:00", "ended_at": "2026-05-19T15:40:49.543143+00:00", "prompt_metrics": {"chars": 30143, "bytes_utf8": 30143, "lines": 795, "estimated_tokens": null}, "response_metrics": {"chars": 747, "bytes_utf8": 747, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 473, "reasoning_output_tokens": 257}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n    SELECT\\n        \\\"class\\\",\\n        SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n        SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT\\n    \\\"class\\\",\\n    CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Applied the requested template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Because the grouping column and condition column are the same, the '0' group yields a NULL ratio (no '1' rows in that group) and the '1' group yields 0.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e3ce1b5f1d653c31651271b7ba751ea348cfe8b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_4069c2f7e4b1c80d",
+  "api_calls": 0,
+  "input_tokens": 20520,
+  "cached_input_tokens": 19840,
+  "output_tokens": 473,
+  "total_tokens": 20993,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 20085.87,
+  "sql_execution_elapsed_ms_total": 5.18,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6b9489de4b0371fe486186aa1e32827b94daf65
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29467, "bytes_utf8": 29467, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 435, "bytes_utf8": 435, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 19840, "output_tokens": 298, "reasoning_output_tokens": 190}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..04773b2453c5aef69fe3e30158addc62fe09287d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_406c8b0b58ed1e68",
+    "api_calls": 0,
+    "input_tokens": 20330,
+    "cached_input_tokens": 19840,
+    "output_tokens": 298,
+    "total_tokens": 20628,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15359.34,
+    "sql_execution_elapsed_ms_total": 11.15,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..93005812400d51d193d909dec5fa72ee38f15156
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:44:21.730591+00:00",
+  "ended_at": "2026-05-19T15:44:37.089972+00:00",
+  "elapsed_ms": 15359.34,
+  "prompt_metrics": {
+    "chars": 29467,
+    "bytes_utf8": 29467,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1123,
+    "bytes_utf8": 1123,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 435,
+      "bytes_utf8": 435,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20330,
+      "cached_input_tokens": 19840,
+      "output_tokens": 298,
+      "reasoning_output_tokens": 190
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc8769f509d01893e25d463d24b1543fca221373
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
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+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_parts": "0",
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+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
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+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
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+      "word_freq_3d": "0",
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+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
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+      "word_freq_money": "0.06",
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+      "word_freq_george": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
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+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
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+      "word_freq_people": "0.31",
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+      "word_freq_credit": "0",
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+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
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+      "word_freq_over": "0",
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+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
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+      "word_freq_credit": "0",
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+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=capital_run_length_longest.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "capital_run_length_longest",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 27.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1ecc5a20d546ce71a5d8dc0b4bf63c6c1b6d90ec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e8-d7f4-75d1-9b61-770eb484ab48"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20965. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"capital_run_length_longest\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"capital_run_length_longest\\\". Cast the measure to REAL because the schema stores numeric-looking values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20330,"cached_input_tokens":19840,"output_tokens":298,"reasoning_output_tokens":190}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20ac68e073d3b6ca5db6b3d27a15ba645a029b5e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"capital_run_length_longest\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"class\" and measure_col=\"capital_run_length_longest\". Cast the measure to REAL because the schema stores numeric-looking values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..908055a669fcabe15a24b550c1162378159ab7a3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=capital_run_length_longest.
+Result preview: [{"class": "1", "avg_measure": 104.39327082184225}, {"class": "0", "avg_measure": 18.214490674318508}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b05017bb2dd9f235796fa0947fb1b8de10ae3e55
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_406c8b0b58ed1e68
+-- problem_id: v2p_n1_620fa31882ab5718
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("capital_run_length_longest" AS REAL)) OVER (PARTITION BY "class") AS avg_measure
+FROM "n1"
+ORDER BY avg_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e02202895fa0d8ebf19159ccf71475907874576a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"capital_run_length_longest\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"capital_run_length_longest\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"1\", \"avg_measure\": 104.39327082184225}, {\"class\": \"0\", \"avg_measure\": 18.214490674318508}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 11.15}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..3bd857060a0b556a7eab70bb3ac3b578d4bb83fa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:44:21.727106+00:00",
+  "ended_at": "2026-05-19T15:44:37.103712+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_406c8b0b58ed1e68",
+    "problem_id": "v2p_n1_620fa31882ab5718",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=capital_run_length_longest.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "capital_run_length_longest",
+      "top_k": 15,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 27.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=55"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_406c8b0b58ed1e68.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_406c8b0b58ed1e68",
+    "api_calls": 0,
+    "input_tokens": 20330,
+    "cached_input_tokens": 19840,
+    "output_tokens": 298,
+    "total_tokens": 20628,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15359.34,
+    "sql_execution_elapsed_ms_total": 11.15,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7cd865cacc7a67d7642c8a2188235ed8949af4c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:44:37.090942+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15359.34, "started_at": "2026-05-19T15:44:21.730591+00:00", "ended_at": "2026-05-19T15:44:37.089972+00:00", "prompt_metrics": {"chars": 29467, "bytes_utf8": 29467, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 435, "bytes_utf8": 435, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 19840, "output_tokens": 298, "reasoning_output_tokens": 190}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"capital_run_length_longest\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"capital_run_length_longest\\\". Cast the measure to REAL because the schema stores numeric-looking values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..88c5a23a692261c7e1b23a41593527f007e98e19
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_406c8b0b58ed1e68",
+  "api_calls": 0,
+  "input_tokens": 20330,
+  "cached_input_tokens": 19840,
+  "output_tokens": 298,
+  "total_tokens": 20628,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 15359.34,
+  "sql_execution_elapsed_ms_total": 11.15,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_406c8b0b58ed1e68/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..054ab6ee0461c83293858f93ed1048bdef888a2d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/run_manifest.json
@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:03:40.564115+00:00",
+  "ended_at": "2026-05-19T16:03:47.788148+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_4076fdb3e0b8bbdd",
+    "problem_id": "v2p_n1_136e125de0ba02c9",
+    "dataset_id": "n1",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "top_k": 16,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=126"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2cf6a11e778f1d15faf737b2631388bdea774e43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:03:43.899786+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3332.6, "started_at": "2026-05-19T16:03:40.566166+00:00", "ended_at": "2026-05-19T16:03:43.898790+00:00", "prompt_metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fa-86c6-7682-acf7-1747d85bfdf0\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:03:47.788063+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2886.22, "started_at": "2026-05-19T16:03:44.901062+00:00", "ended_at": "2026-05-19T16:03:47.787318+00:00", "prompt_metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fa-9792-7ed2-9d08-2e8a9669b6de\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..54c493a483caf9aa2fa1acd82ce0a12a4ea5fa68
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29355, "bytes_utf8": 29355, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 349, "bytes_utf8": 349, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20317, "cached_input_tokens": 12032, "output_tokens": 528, "reasoning_output_tokens": 432}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f4fd76370b0f7b05713b8f5a20566d306ef9ddb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_42c8d342246483ca",
+    "api_calls": 0,
+    "input_tokens": 20317,
+    "cached_input_tokens": 12032,
+    "output_tokens": 528,
+    "total_tokens": 20845,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 22829.93,
+    "sql_execution_elapsed_ms_total": 2.29,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f1ee98da9aed0544c9d7a99e24b7c274e9ad74e1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:29:34.913738+00:00",
+  "ended_at": "2026-05-19T15:29:57.743693+00:00",
+  "elapsed_ms": 22829.93,
+  "prompt_metrics": {
+    "chars": 29355,
+    "bytes_utf8": 29355,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1702,
+    "bytes_utf8": 1702,
+    "lines": 7,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 349,
+      "bytes_utf8": 349,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20317,
+      "cached_input_tokens": 12032,
+      "output_tokens": 528,
+      "reasoning_output_tokens": 432
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..476f178186cade5608b92c126f2366b9b876bb33
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_our.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_our",
+    "top_k": 14,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.38,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e019f790d5a9f983cc6303ae073d765e831f3109
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,7 @@
+{"type":"thread.started","thread_id":"019e40db-4fdb-7652-be8e-8b443ec70207"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20952. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20952. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20952. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_our\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the planned grouped-sum template with \\\"class\\\" as the group column and cast \\\"word_freq_our\\\" to REAL because the schema stores numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20317,"cached_input_tokens":12032,"output_tokens":528,"reasoning_output_tokens":432}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50fd6566dd7a741393b41f70b251471dd3d4e0b0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_our\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Used the planned grouped-sum template with \"class\" as the group column and cast \"word_freq_our\" to REAL because the schema stores numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_42c8d342246483ca/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e6a1e6be6bf3158e58999063a9cec5080416ed36
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:43.934771+00:00",
+  "ended_at": "2026-05-19T16:04:47.120883+00:00",
+  "elapsed_ms": 3186.09,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8dd364abf7d598b2474df92e645e7f010ed6dfd8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:48.123069+00:00",
+  "ended_at": "2026-05-19T16:04:51.028814+00:00",
+  "elapsed_ms": 2905.71,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fb733de1da1b4714e1053f7ecec584581c96d40
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fb733de1da1b4714e1053f7ecec584581c96d40
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e0a8b24fcb190c8ab591a35ecaf99e4580e944df
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-7e2d-70f1-9f44-be0999d6bb66"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca8934779e11030a01f5b5a126e1688a2170f387
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-7e2d-70f1-9f44-be0999d6bb66"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a9063ece2fdff2643289f085bdf85a5ca656ff8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-8e81-7331-9837-1fd2d280dfa0"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa8d17bcd7266b0d8bec4d047f3e95dcba15f1c0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-8e81-7331-9837-1fd2d280dfa0"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_43cd0c685056cd65/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..02abb10f302cf79bb8231e3e83f6f2ba4ec5ca30
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_money.
+Result preview: [{"class": "1", "total_measure": 385.95}, {"class": "0", "total_measure": 47.78}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..05d9478f3013b2b301ca65f26014ac85cc088883
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/generated_sql.sql
@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_tpch_thresholded_group_ranking
+-- query_record_id: v2q_n1_4d17d0fd5ebc19a7
+-- problem_id: v2p_n1_6a960495c2399818
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_money" AS REAL)) AS "total_measure"
+FROM "n1"
+GROUP BY "class"
+HAVING SUM(CAST("word_freq_money" AS REAL)) > 0.0
+ORDER BY "total_measure" DESC
+LIMIT 17;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..47191e8eb3f18e2990919601b7f9f4a1ceb84e4a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_money\" AS REAL)) > 0.0\nORDER BY \"total_measure\" DESC\nLIMIT 17;", "result": "{\"query\": \"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_money\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 17;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 385.95}, {\"class\": \"0\", \"total_measure\": 47.78}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.37}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2620325125817aa5c83b112be747fe43c0a428b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:05:45.456313+00:00",
+  "ended_at": "2026-05-19T16:05:58.755851+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_4d17d0fd5ebc19a7",
+    "problem_id": "v2p_n1_6a960495c2399818",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_money.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_money",
+      "top_k": 17,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=2/2",
+      "binding_index=137"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_4d17d0fd5ebc19a7.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_4d17d0fd5ebc19a7",
+    "api_calls": 0,
+    "input_tokens": 20359,
+    "cached_input_tokens": 12032,
+    "output_tokens": 391,
+    "total_tokens": 20750,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12289.42,
+    "sql_execution_elapsed_ms_total": 2.37,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aee75e48ac204f1f7928e8d7ab722f6bf28df3d2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:05:49.046697+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3586.15, "started_at": "2026-05-19T16:05:45.459561+00:00", "ended_at": "2026-05-19T16:05:49.045741+00:00", "prompt_metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fc-6e88-7b52-897a-a349c1ea8e8d\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:05:58.752099+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8703.27, "started_at": "2026-05-19T16:05:50.047903+00:00", "ended_at": "2026-05-19T16:05:58.751209+00:00", "prompt_metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 489, "bytes_utf8": 489, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 12032, "output_tokens": 391, "reasoning_output_tokens": 260}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_money\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 17;\",\"notes\":\"Used the required Thresholded Group Ranking template with \\\"class\\\" as the group column and \\\"word_freq_money\\\" as the measure column. CAST to REAL is included because the schema stores feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c80a43443349f56435604a0222f438e1d43efdd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_4d17d0fd5ebc19a7",
+  "api_calls": 0,
+  "input_tokens": 20359,
+  "cached_input_tokens": 12032,
+  "output_tokens": 391,
+  "total_tokens": 20750,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 2,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 12289.42,
+  "sql_execution_elapsed_ms_total": 2.37,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2e2094247f69a85df0fedf027a4638210fca931c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"word_freq_receive": "0", "support": 3892, "avg_response": 0.2560919835560123}, {"word_freq_receive": "0.1", "support": 27, "avg_response": 0.4585185185185185}, {"word_freq_receive": "0.26", "support": 26, "avg_response": 0.8273076923076922}, {"word_freq_receive": "0.17", "support": 25, "avg_response": 0.5783999999999999}, {"word_freq_receive": "0.3", "support": 24, "avg_response": 0.6841666666666667}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..3413daa10e1428b8f483d31c4dfb143214161f45
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n1_57bdf32725ca7364
+-- problem_id: v2p_n1_4086986c8b328762
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "word_freq_receive",
+    COUNT(*) AS support,
+    AVG("word_freq_all") AS avg_response
+FROM "n1"
+GROUP BY "word_freq_receive"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1b3257ccd22f099a98243a36b178e63ff9f6961
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n1_57bdf32725ca7364\n-- problem_id: v2p_n1_4086986c8b328762\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"word_freq_receive\",\n    COUNT(*) AS support,\n    AVG(\"word_freq_all\") AS avg_response\nFROM \"n1\"\nGROUP BY \"word_freq_receive\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n1_57bdf32725ca7364\\n-- problem_id: v2p_n1_4086986c8b328762\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"word_freq_receive\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"word_freq_all\\\") AS avg_response\\nFROM \\\"n1\\\"\\nGROUP BY \\\"word_freq_receive\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"word_freq_receive\", \"support\", \"avg_response\"], \"rows\": [{\"word_freq_receive\": \"0\", \"support\": 3892, \"avg_response\": 0.2560919835560123}, {\"word_freq_receive\": \"0.1\", \"support\": 27, \"avg_response\": 0.4585185185185185}, {\"word_freq_receive\": \"0.26\", \"support\": 26, \"avg_response\": 0.8273076923076922}, {\"word_freq_receive\": \"0.17\", \"support\": 25, \"avg_response\": 0.5783999999999999}, {\"word_freq_receive\": \"0.3\", \"support\": 24, \"avg_response\": 0.6841666666666667}, {\"word_freq_receive\": \"0.29\", \"support\": 22, \"avg_response\": 0.34727272727272723}, {\"word_freq_receive\": \"0.14\", \"support\": 20, \"avg_response\": 0.365}, {\"word_freq_receive\": \"0.31\", \"support\": 19, \"avg_response\": 0.2942105263157895}, {\"word_freq_receive\": \"0.08\", \"support\": 19, \"avg_response\": 0.29157894736842105}, {\"word_freq_receive\": \"0.23\", \"support\": 18, \"avg_response\": 0.3977777777777778}, {\"word_freq_receive\": \"0.11\", \"support\": 18, \"avg_response\": 0.2977777777777778}, {\"word_freq_receive\": \"0.12\", \"support\": 16, \"avg_response\": 0.59625}, {\"word_freq_receive\": \"0.25\", \"support\": 16, \"avg_response\": 0.411875}, {\"word_freq_receive\": \"0.09\", \"support\": 16, \"avg_response\": 0.3075}, {\"word_freq_receive\": \"0.34\", \"support\": 16, \"avg_response\": 0.29625}, {\"word_freq_receive\": \"0.38\", \"support\": 15, \"avg_response\": 0.61}, {\"word_freq_receive\": \"0.39\", \"support\": 15, \"avg_response\": 0.3253333333333333}, {\"word_freq_receive\": \"0.27\", \"support\": 14, \"avg_response\": 0.53}, {\"word_freq_receive\": \"0.36\", \"support\": 13, \"avg_response\": 0.836923076923077}, {\"word_freq_receive\": \"0.16\", \"support\": 13, \"avg_response\": 0.44153846153846155}, {\"word_freq_receive\": \"0.48\", \"support\": 13, \"avg_response\": 0.2915384615384615}, {\"word_freq_receive\": \"0.2\", \"support\": 12, \"avg_response\": 0.4825}, {\"word_freq_receive\": \"0.05\", \"support\": 12, \"avg_response\": 0.1791666666666667}, {\"word_freq_receive\": \"0.33\", \"support\": 11, \"avg_response\": 0.5654545454545455}, {\"word_freq_receive\": \"0.28\", \"support\": 11, \"avg_response\": 0.4081818181818182}, {\"word_freq_receive\": \"2\", \"support\": 11, \"avg_response\": 0.0}, {\"word_freq_receive\": \"0.4\", \"support\": 10, \"avg_response\": 0.06000000000000001}, {\"word_freq_receive\": \"0.24\", \"support\": 9, \"avg_response\": 0.6322222222222221}, {\"word_freq_receive\": \"0.21\", \"support\": 9, \"avg_response\": 0.5155555555555555}, {\"word_freq_receive\": \"0.15\", \"support\": 9, \"avg_response\": 0.32333333333333336}, {\"word_freq_receive\": \"0.06\", \"support\": 9, \"avg_response\": 0.21888888888888888}, {\"word_freq_receive\": \"0.22\", \"support\": 8, \"avg_response\": 0.485}, {\"word_freq_receive\": \"0.72\", \"support\": 8, \"avg_response\": 0.46625}, {\"word_freq_receive\": \"0.19\", \"support\": 8, \"avg_response\": 0.295}, {\"word_freq_receive\": \"0.47\", \"support\": 8, \"avg_response\": 0.17625}, {\"word_freq_receive\": \"0.54\", \"support\": 8, \"avg_response\": 0.03375}, {\"word_freq_receive\": \"0.35\", \"support\": 7, \"avg_response\": 0.35857142857142854}, {\"word_freq_receive\": \"0.37\", \"support\": 6, \"avg_response\": 1.085}, {\"word_freq_receive\": \"0.32\", \"support\": 6, \"avg_response\": 0.7183333333333333}, {\"word_freq_receive\": \"0.42\", \"support\": 6, \"avg_response\": 0.6966666666666668}, {\"word_freq_receive\": \"0.07\", \"support\": 6, \"avg_response\": 0.5283333333333333}, {\"word_freq_receive\": \"0.5\", \"support\": 6, \"avg_response\": 0.5216666666666666}, {\"word_freq_receive\": \"0.13\", \"support\": 6, \"avg_response\": 0.22666666666666668}, {\"word_freq_receive\": \"0.74\", \"support\": 6, \"avg_response\": 0.18500000000000003}, {\"word_freq_receive\": \"0.41\", \"support\": 5, \"avg_response\": 0.466}, {\"word_freq_receive\": \"0.58\", \"support\": 5, \"avg_response\": 0.46399999999999997}, {\"word_freq_receive\": \"0.46\", \"support\": 5, \"avg_response\": 0.4600000000000001}, {\"word_freq_receive\": \"0.18\", \"support\": 5, \"avg_response\": 0.358}, {\"word_freq_receive\": \"0.64\", \"support\": 5, \"avg_response\": 0.178}, {\"word_freq_receive\": \"0.02\", \"support\": 5, \"avg_response\": 0.166}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.19}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..f51a7c19ca208719d4d23f7004584001974bf647
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/run_manifest.json
@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.189444+00:00",
+  "ended_at": "2026-05-19T16:06:30.192403+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_57bdf32725ca7364",
+    "problem_id": "v2p_n1_4086986c8b328762",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=word_freq_all, key_col=word_freq_receive.",
+    "bindings": {
+      "key_col": "word_freq_receive",
+      "measure_col": "word_freq_all",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=7",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_57bdf32725ca7364.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_57bdf32725ca7364/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6c9d28eaeb87dd813e33a72dd2f2ceaeaaf8a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29920, "bytes_utf8": 29920, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 644, "bytes_utf8": 644, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 354, "reasoning_output_tokens": 189}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5caa269b03b6e04933b9f33b8a4ac18c32b8bc77
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_58cca2cc5c105239",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 19840,
+    "output_tokens": 354,
+    "total_tokens": 20805,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13873.85,
+    "sql_execution_elapsed_ms_total": 5.59,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b12af6cb13c7cad99f210492b2bc7354212e1913
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:47:01.104568+00:00",
+  "ended_at": "2026-05-19T15:47:14.978452+00:00",
+  "elapsed_ms": 13873.85,
+  "prompt_metrics": {
+    "chars": 29920,
+    "bytes_utf8": 29920,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1343,
+    "bytes_utf8": 1343,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 644,
+      "bytes_utf8": 644,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20451,
+      "cached_input_tokens": 19840,
+      "output_tokens": 354,
+      "reasoning_output_tokens": 189
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..07b7885bf8d323d20f290cb2dcd6436eb8e5e2d9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_business.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_business",
+    "top_k": 18,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1eb42ca79a5bda946049f0926674e4ba657911d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40eb-4693-7d61-a239-f1d0286f1645"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1980530, Requested 21086. Please try again in 48ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_business\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the requested template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_business\\\". Cast the measure from TEXT to REAL because the schema snapshot types numeric-looking fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":19840,"output_tokens":354,"reasoning_output_tokens":189}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..823e165ea6ce7c08cd6ae97b23bf4d0b437f369d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_business\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Used the requested template with group_col=\"class\" and measure_col=\"word_freq_business\". Cast the measure from TEXT to REAL because the schema snapshot types numeric-looking fields as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4acea20e2c9712185936734473d15aa9f529e26e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_business.
+Result preview: [{"class": "1", "group_value": 521.25}, {"class": "0", "group_value": 134.79}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c8241c3c13a5c46e5145779e4164b3653208359b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_58cca2cc5c105239
+-- problem_id: v2p_n1_b8c48bb3d2f95988
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_business" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8c165044c3a17f917378d1d9523076111284c120
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_business\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_business\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 521.25}, {\"class\": \"0\", \"group_value\": 134.79}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.59}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8f56c4dbe8de9a598ddb547d0cce51bdadfe11c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:47:01.102616+00:00",
+  "ended_at": "2026-05-19T15:47:14.989945+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_58cca2cc5c105239",
+    "problem_id": "v2p_n1_b8c48bb3d2f95988",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_business.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_business",
+      "top_k": 18,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=2/2",
+      "binding_index=73"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_58cca2cc5c105239.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_58cca2cc5c105239",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 19840,
+    "output_tokens": 354,
+    "total_tokens": 20805,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13873.85,
+    "sql_execution_elapsed_ms_total": 5.59,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbd0a2d2960c6633ed7d8a1b14a2d0c94042e24b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:47:14.980390+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13873.85, "started_at": "2026-05-19T15:47:01.104568+00:00", "ended_at": "2026-05-19T15:47:14.978452+00:00", "prompt_metrics": {"chars": 29920, "bytes_utf8": 29920, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 644, "bytes_utf8": 644, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 354, "reasoning_output_tokens": 189}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_business\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the requested template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_business\\\". Cast the measure from TEXT to REAL because the schema snapshot types numeric-looking fields as TEXT in SQLite.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..10151a1faeadf57e281364ba5db0b52303c4b180
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_58cca2cc5c105239",
+  "api_calls": 0,
+  "input_tokens": 20451,
+  "cached_input_tokens": 19840,
+  "output_tokens": 354,
+  "total_tokens": 20805,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 13873.85,
+  "sql_execution_elapsed_ms_total": 5.59,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_58cca2cc5c105239/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..870db9a3c2ddaa93c5b5369e6ff6c56b1b669fd0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_data.
+Result preview: [{"class": "1", "word_freq_415": "0", "total_measure": 26.4, "share_within_group": 100.0}, {"class": "0", "word_freq_415": "0", "total_measure": 402.77, "share_within_group": 95.6811972918399}, {"class": "0", "word_freq_415": "0.25", "total_measure": 2.83, "share_within_group": 0.672288870412163}, {"class": "0", "word_freq_415": "0.53", "total_measure": 1.88, "share_within_group": 0.4466088609098468}, {"class": "0", "word_freq_415": "0.58", "total_measure": 1.76, "share_within_group": 0.41810191234113314}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..03f7c5e8e426379ac1ee4849abe9b1aa8b55b4a6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_5aeecbc5027a1afb
+-- problem_id: v2p_n1_77bde67c28825b81
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "class",
+  "word_freq_415",
+  SUM(CAST("word_freq_data" AS REAL)) AS total_measure,
+  SUM(CAST("word_freq_data" AS REAL)) * 100.0 / SUM(SUM(CAST("word_freq_data" AS REAL))) OVER (PARTITION BY "class") AS share_within_group
+FROM "n1"
+GROUP BY "class", "word_freq_415"
+ORDER BY share_within_group DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1abd23942a2527ef64023c68de31d784006dd69c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_415\",\n  SUM(CAST(\"word_freq_data\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_data\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_data\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_415\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_415\\\",\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_data\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_415\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"class\", \"word_freq_415\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_415\": \"0\", \"total_measure\": 26.4, \"share_within_group\": 100.0}, {\"class\": \"0\", \"word_freq_415\": \"0\", \"total_measure\": 402.77, \"share_within_group\": 95.6811972918399}, {\"class\": \"0\", \"word_freq_415\": \"0.25\", \"total_measure\": 2.83, \"share_within_group\": 0.672288870412163}, {\"class\": \"0\", \"word_freq_415\": \"0.53\", \"total_measure\": 1.88, \"share_within_group\": 0.4466088609098468}, {\"class\": \"0\", \"word_freq_415\": \"0.58\", \"total_measure\": 1.76, \"share_within_group\": 0.41810191234113314}, {\"class\": \"0\", \"word_freq_415\": \"0.33\", \"total_measure\": 1.6700000000000002, \"share_within_group\": 0.396721700914598}, {\"class\": \"0\", \"word_freq_415\": \"0.26\", \"total_measure\": 1.3, \"share_within_group\": 0.30882527616106425}, {\"class\": \"0\", \"word_freq_415\": \"0.15\", \"total_measure\": 1.21, \"share_within_group\": 0.2874450647345291}, {\"class\": \"0\", \"word_freq_415\": \"1.2\", \"total_measure\": 1.2, \"share_within_group\": 0.28506948568713625}, {\"class\": \"0\", \"word_freq_415\": \"0.09\", \"total_measure\": 1.12, \"share_within_group\": 0.26606485330799384}, {\"class\": \"0\", \"word_freq_415\": \"0.24\", \"total_measure\": 0.85, \"share_within_group\": 0.20192421902838817}, {\"class\": \"0\", \"word_freq_415\": \"0.19\", \"total_measure\": 0.79, \"share_within_group\": 0.18767074474403136}, {\"class\": \"0\", \"word_freq_415\": \"0.28\", \"total_measure\": 0.71, \"share_within_group\": 0.16866611236488896}, {\"class\": \"0\", \"word_freq_415\": \"0.32\", \"total_measure\": 0.64, \"share_within_group\": 0.15203705903313933}, {\"class\": \"0\", \"word_freq_415\": \"0.55\", \"total_measure\": 0.55, \"share_within_group\": 0.13065684760660412}, {\"class\": \"0\", \"word_freq_415\": \"0.17\", \"total_measure\": 0.52, \"share_within_group\": 0.1235301104644257}, {\"class\": \"0\", \"word_freq_415\": \"0.2\", \"total_measure\": 0.41, \"share_within_group\": 0.09739874094310488}, {\"class\": \"0\", \"word_freq_415\": \"0.39\", \"total_measure\": 0.39, \"share_within_group\": 0.09264758284831928}, {\"class\": \"0\", \"word_freq_415\": \"0.35\", \"total_measure\": 0.35, \"share_within_group\": 0.08314526665874807}, {\"class\": \"0\", \"word_freq_415\": \"0.11\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.13\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.16\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.22\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.23\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.27\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.29\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.34\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.37\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.38\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.41\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.42\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.43\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.44\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.45\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.46\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.47\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.48\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.49\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.5\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.51\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.52\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.54\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.59\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.6\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.61\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.62\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.63\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.64\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.65\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_415\": \"0.66\", \"total_measure\": 0.0, \"share_within_group\": 0.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 3.53}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e7818392fecb13039d5ddad98dfeeb365ebed74b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:38:38.119793+00:00",
+  "ended_at": "2026-05-19T15:38:52.035291+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_5aeecbc5027a1afb",
+    "problem_id": "v2p_n1_77bde67c28825b81",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_data.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_data",
+      "item_col": "word_freq_415",
+      "top_k": 17,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=9",
+      "sql_variant_index=2/2",
+      "binding_index=32"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 9,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_5aeecbc5027a1afb.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_5aeecbc5027a1afb",
+    "api_calls": 0,
+    "input_tokens": 20435,
+    "cached_input_tokens": 19840,
+    "output_tokens": 425,
+    "total_tokens": 20860,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13906.97,
+    "sql_execution_elapsed_ms_total": 3.53,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69966142bebcf5d2f7886231e3ad1c5f2b4d9f53
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:38:52.030329+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13906.97, "started_at": "2026-05-19T15:38:38.122513+00:00", "ended_at": "2026-05-19T15:38:52.029511+00:00", "prompt_metrics": {"chars": 29768, "bytes_utf8": 29768, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 635, "bytes_utf8": 635, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20435, "cached_input_tokens": 19840, "output_tokens": 425, "reasoning_output_tokens": 246}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_415\\\",\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_data\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_data\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_415\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_415\\\", and measure_col=\\\"word_freq_data\\\". CAST to REAL is used because the schema stores these numeric fields as TEXT in SQLite.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..8db800f561f7c7ab56afedc60abd8e72ce257fb7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_5aeecbc5027a1afb",
+  "api_calls": 0,
+  "input_tokens": 20435,
+  "cached_input_tokens": 19840,
+  "output_tokens": 425,
+  "total_tokens": 20860,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 13906.97,
+  "sql_execution_elapsed_ms_total": 3.53,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f40bc46dd36dba62fce68766e3a7cc315d32d6df
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29537, "bytes_utf8": 29537, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1710, "bytes_utf8": 1710, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20353, "cached_input_tokens": 19840, "output_tokens": 1515, "reasoning_output_tokens": 934}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..8fc0abbfcd0941a51288e93802332237dca24009
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_5d8d8939fbb670a8",
+    "api_calls": 0,
+    "input_tokens": 20353,
+    "cached_input_tokens": 19840,
+    "output_tokens": 1515,
+    "total_tokens": 21868,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 21493.29,
+    "sql_execution_elapsed_ms_total": 22.48,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..3aceff3a47d9de56fd576039e2c5c7717488cdfa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:55:47.681180+00:00",
+  "ended_at": "2026-05-19T15:56:09.174502+00:00",
+  "elapsed_ms": 21493.29,
+  "prompt_metrics": {
+    "chars": 29537,
+    "bytes_utf8": 29537,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2313,
+    "bytes_utf8": 2313,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1710,
+      "bytes_utf8": 1710,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20353,
+      "cached_input_tokens": 19840,
+      "output_tokens": 1515,
+      "reasoning_output_tokens": 934
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f46ccac6d7ff58aeef1afb950882bb347d61702e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_data.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_data",
+    "top_k": 19,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0b6bef393595155874e4942a1560c94bd49ccf66
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-4f74-7d20-8504-d36df27d4773"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_data\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_data\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"class\\\") AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"word_freq_data\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    \\\"measure\\\",\\n    \\\"rn\\\",\\n    ((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS \\\"p\\\",\\n    CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) AS \\\"floor_rn\\\",\\n    CASE\\n      WHEN ((0.95 * (\\\"cnt\\\" - 1)) + 1.0) = CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) THEN CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER)\\n      ELSE CAST(((0.95 * (\\\"cnt\\\" - 1)) + 1.0) AS INTEGER) + 1\\n    END AS \\\"ceil_rn\\\"\\n  FROM \\\"ranked\\\"\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    MAX(CASE WHEN \\\"rn\\\" = \\\"floor_rn\\\" THEN \\\"measure\\\" END) AS \\\"floor_val\\\",\\n    MAX(CASE WHEN \\\"rn\\\" = \\\"ceil_rn\\\" THEN \\\"measure\\\" END) AS \\\"ceil_val\\\",\\n    MAX(\\\"p\\\") AS \\\"p\\\",\\n    MAX(\\\"floor_rn\\\") AS \\\"floor_rn\\\",\\n    MAX(\\\"ceil_rn\\\") AS \\\"ceil_rn\\\"\\n  FROM \\\"positions\\\"\\n  GROUP BY \\\"class\\\"\\n)\\nSELECT\\n  \\\"class\\\",\\n  CASE\\n    WHEN \\\"floor_rn\\\" = \\\"ceil_rn\\\" THEN \\\"floor_val\\\"\\n    ELSE \\\"floor_val\\\" + ((\\\"p\\\" - \\\"floor_rn\\\") * (\\\"ceil_val\\\" - \\\"floor_val\\\"))\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite has no PERCENTILE_CONT, so this emulates the 95th percentile of \\\"word_freq_data\\\" within each \\\"class\\\" using window functions and linear interpolation. \\\"word_freq_data\\\" is cast from TEXT to REAL to preserve numeric ordering.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20353,"cached_input_tokens":19840,"output_tokens":1515,"reasoning_output_tokens":934}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fce25fbb95e55b3d30641c4e2aca75c3fb9393b3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_data\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_data\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"class\") AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"word_freq_data\" IS NOT NULL\n),\n\"positions\" AS (\n  SELECT\n    \"class\",\n    \"measure\",\n    \"rn\",\n    ((0.95 * (\"cnt\" - 1)) + 1.0) AS \"p\",\n    CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER) AS \"floor_rn\",\n    CASE\n      WHEN ((0.95 * (\"cnt\" - 1)) + 1.0) = CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER) THEN CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER)\n      ELSE CAST(((0.95 * (\"cnt\" - 1)) + 1.0) AS INTEGER) + 1\n    END AS \"ceil_rn\"\n  FROM \"ranked\"\n),\n\"picked\" AS (\n  SELECT\n    \"class\",\n    MAX(CASE WHEN \"rn\" = \"floor_rn\" THEN \"measure\" END) AS \"floor_val\",\n    MAX(CASE WHEN \"rn\" = \"ceil_rn\" THEN \"measure\" END) AS \"ceil_val\",\n    MAX(\"p\") AS \"p\",\n    MAX(\"floor_rn\") AS \"floor_rn\",\n    MAX(\"ceil_rn\") AS \"ceil_rn\"\n  FROM \"positions\"\n  GROUP BY \"class\"\n)\nSELECT\n  \"class\",\n  CASE\n    WHEN \"floor_rn\" = \"ceil_rn\" THEN \"floor_val\"\n    ELSE \"floor_val\" + ((\"p\" - \"floor_rn\") * (\"ceil_val\" - \"floor_val\"))\n  END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite has no PERCENTILE_CONT, so this emulates the 95th percentile of \"word_freq_data\" within each \"class\" using window functions and linear interpolation. \"word_freq_data\" is cast from TEXT to REAL to preserve numeric ordering."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5d8d8939fbb670a8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ef2be0552b28d4edeeb2052b50fbae9ecf6dc2a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29356, "bytes_utf8": 29356, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 385, "bytes_utf8": 385, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 444, "reasoning_output_tokens": 345}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c3775241113f7e262b76c50c8ad964c0d9772665
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_5e07a5fa82877d81",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 444,
+    "total_tokens": 20759,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10939.69,
+    "sql_execution_elapsed_ms_total": 5.14,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e49371f0fec86ae77a803d176fbf2c0fb8599595
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:29:03.787688+00:00",
+  "ended_at": "2026-05-19T15:29:14.727407+00:00",
+  "elapsed_ms": 10939.69,
+  "prompt_metrics": {
+    "chars": 29356,
+    "bytes_utf8": 29356,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 739,
+    "bytes_utf8": 739,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 385,
+      "bytes_utf8": 385,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20315,
+      "cached_input_tokens": 12032,
+      "output_tokens": 444,
+      "reasoning_output_tokens": 345
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ddb9f16cbf69eb644159fc6b4c2584ff4abd36cb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_all.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_all",
+    "top_k": 17,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.25,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6295edb9565df501afd8cc7485cf24afdbc4e179
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-d613-78e2-a64b-290105ee83be"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_all\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Uses the requested grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_all\\\". CAST to REAL is included because the schema snapshot types this measure column as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":444,"reasoning_output_tokens":345}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa6c702145882b8dc0706e21a41b28198a3ade22
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_all\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Uses the requested grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_all\". CAST to REAL is included because the schema snapshot types this measure column as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5e07a5fa82877d81/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a1ee5eb882315e6f2568327371fccdf11aed326c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1504, "bytes_utf8": 1504, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 1995, "reasoning_output_tokens": 1512}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..318a07685802b9d76945a5127ee992c95c3fb58f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_60582ed2778ea487",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 19840,
+    "output_tokens": 1995,
+    "total_tokens": 22350,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 30916.72,
+    "sql_execution_elapsed_ms_total": 21.45,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d2ed2578d3b20c00caafefd835aded68e3a024f0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:51:15.511333+00:00",
+  "ended_at": "2026-05-19T15:51:46.428081+00:00",
+  "elapsed_ms": 30916.72,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2067,
+    "bytes_utf8": 2067,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1504,
+      "bytes_utf8": 1504,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 19840,
+      "output_tokens": 1995,
+      "reasoning_output_tokens": 1512
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cfe7db6f746c8dc2f92147312c2f285c55a1d7bd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_650.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_650",
+    "top_k": 19,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..58f9fc749bd1b0bccf95305e6113fa0a367fd0aa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ef-2841-7c73-8254-c5450c6e90f9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_650\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_650\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"class\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_650\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"class\\\",\\n    1 + CAST(((\\\"cnt\\\" - 1) * 9) / 10 AS INT) AS \\\"lower_rn\\\",\\n    1 + CAST((((\\\"cnt\\\" - 1) * 9) + 9) / 10 AS INT) AS \\\"upper_rn\\\",\\n    (((\\\"cnt\\\" - 1) * 9) % 10) / 10.0 AS \\\"frac\\\"\\n  FROM \\\"ranked\\\"\\n),\\n\\\"bounds\\\" AS (\\n  SELECT\\n    p.\\\"class\\\",\\n    p.\\\"frac\\\",\\n    MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN r.\\\"measure\\\" END) AS \\\"lower_val\\\",\\n    MAX(CASE WHEN r.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN r.\\\"measure\\\" END) AS \\\"upper_val\\\"\\n  FROM \\\"positions\\\" AS p\\n  JOIN \\\"ranked\\\" AS r\\n    ON r.\\\"class\\\" = p.\\\"class\\\"\\n  GROUP BY p.\\\"class\\\", p.\\\"frac\\\"\\n)\\nSELECT\\n  \\\"class\\\",\\n  \\\"lower_val\\\" + \\\"frac\\\" * (\\\"upper_val\\\" - \\\"lower_val\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite-compatible rewrite of the grouped percentile template: computes the 90th percentile of \\\"word_freq_650\\\" within each \\\"class\\\" by emulating continuous percentile interpolation with window functions. \\\"word_freq_650\\\" is cast from TEXT to REAL for numeric ordering.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":1995,"reasoning_output_tokens":1512}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..55e1c201d7d2787edec2c5299c8a4da5e971756e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_650\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_650\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"class\"\n    ) AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_650\" IS NOT NULL\n),\n\"positions\" AS (\n  SELECT DISTINCT\n    \"class\",\n    1 + CAST(((\"cnt\" - 1) * 9) / 10 AS INT) AS \"lower_rn\",\n    1 + CAST((((\"cnt\" - 1) * 9) + 9) / 10 AS INT) AS \"upper_rn\",\n    (((\"cnt\" - 1) * 9) % 10) / 10.0 AS \"frac\"\n  FROM \"ranked\"\n),\n\"bounds\" AS (\n  SELECT\n    p.\"class\",\n    p.\"frac\",\n    MAX(CASE WHEN r.\"rn\" = p.\"lower_rn\" THEN r.\"measure\" END) AS \"lower_val\",\n    MAX(CASE WHEN r.\"rn\" = p.\"upper_rn\" THEN r.\"measure\" END) AS \"upper_val\"\n  FROM \"positions\" AS p\n  JOIN \"ranked\" AS r\n    ON r.\"class\" = p.\"class\"\n  GROUP BY p.\"class\", p.\"frac\"\n)\nSELECT\n  \"class\",\n  \"lower_val\" + \"frac\" * (\"upper_val\" - \"lower_val\") AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite-compatible rewrite of the grouped percentile template: computes the 90th percentile of \"word_freq_650\" within each \"class\" by emulating continuous percentile interpolation with window functions. \"word_freq_650\" is cast from TEXT to REAL for numeric ordering."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_60582ed2778ea487/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7b5822c13e87ca2bbd018bc76afd9893d0ff7d4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29437, "bytes_utf8": 29437, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 423, "bytes_utf8": 423, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20327, "cached_input_tokens": 12032, "output_tokens": 280, "reasoning_output_tokens": 174}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..fcf5e42abb6c22eee3123087b1a1c9c525763dd0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_610c13001534f920",
+    "api_calls": 0,
+    "input_tokens": 20327,
+    "cached_input_tokens": 12032,
+    "output_tokens": 280,
+    "total_tokens": 20607,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8826.72,
+    "sql_execution_elapsed_ms_total": 20.58,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..df2b77bff4f90976e1d84bd559a3a55b437e3e75
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:41:41.519931+00:00",
+  "ended_at": "2026-05-19T15:41:50.346686+00:00",
+  "elapsed_ms": 8826.72,
+  "prompt_metrics": {
+    "chars": 29437,
+    "bytes_utf8": 29437,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 777,
+    "bytes_utf8": 777,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 423,
+      "bytes_utf8": 423,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20327,
+      "cached_input_tokens": 12032,
+      "output_tokens": 280,
+      "reasoning_output_tokens": 174
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9cae56eba6b499a85e3776a1183ca522ed6da878
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%28.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%28",
+    "top_k": 14,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.188,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e417830767132f6d931a2051a7ca22e9b983cf44
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e6-661b-7633-9076-f330a06fa061"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%28\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%28\\\". CAST to REAL is included because the schema snapshot shows this measure stored as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20327,"cached_input_tokens":12032,"output_tokens":280,"reasoning_output_tokens":174}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3c4e26c8979f7e4d9c383d6beaa4015e70ac175e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%28\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Uses the requested Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%28\". CAST to REAL is included because the schema snapshot shows this measure stored as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_610c13001534f920/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c23da2b7c7bc5dfbb9edeb8ed95e0304f7dbef6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:50.236713+00:00",
+  "ended_at": "2026-05-19T16:00:53.413479+00:00",
+  "elapsed_ms": 3176.74,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29324,
+    "bytes_utf8": 29324,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..af80b29ffc14b04afbb4e24216510e5e1d5122d2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:54.416472+00:00",
+  "ended_at": "2026-05-19T16:00:57.874795+00:00",
+  "elapsed_ms": 3458.27,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29324,
+    "bytes_utf8": 29324,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e2f9300a6fd95efe8eee89618b00464c47d703f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=capital_run_length_average.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "capital_run_length_average",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 3.706,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e2f9300a6fd95efe8eee89618b00464c47d703f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
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+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
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+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=capital_run_length_average.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "capital_run_length_average",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 3.706,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..763e01b11488aea247b95b3ddc53a9bd352944f5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-ed4d-7620-9a75-943fab67833d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..38e478eaa92807b9c7bb49b35e640abb26235647
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-ed4d-7620-9a75-943fab67833d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3299fe526d3b2ac3b8f3821e511f9dbfe31c726b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-fda0-71e3-b91d-2c7294396ef9"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6dcc30a052112a849cd2a9b4c7a3ed4d5d85e302
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-fda0-71e3-b91d-2c7294396ef9"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_61717ea8fb851583/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92c0693fda0c9a25877207bdcb725d777bd5a976
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29772, "bytes_utf8": 29772, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 658, "bytes_utf8": 658, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 535, "reasoning_output_tokens": 350}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..768b83e824818932d238b21b7d9ea7b1acb2cf2e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_62065963b9f8c10c",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 535,
+    "total_tokens": 20972,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18536.32,
+    "sql_execution_elapsed_ms_total": 4.86,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..378855ecf5d22236b42387f7a29a1368a0b565cf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:37:32.787522+00:00",
+  "ended_at": "2026-05-19T15:37:51.323881+00:00",
+  "elapsed_ms": 18536.32,
+  "prompt_metrics": {
+    "chars": 29772,
+    "bytes_utf8": 29772,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1371,
+    "bytes_utf8": 1371,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 658,
+      "bytes_utf8": 658,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 12032,
+      "output_tokens": 535,
+      "reasoning_output_tokens": 350
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1809588569c25641e2eacd712779917ed17e9639
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_telnet.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_telnet",
+    "item_col": "word_freq_857",
+    "top_k": 15,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9aa262cf5e8cbcffa8edcbf07aef5706ac20b7ba
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e2-9a6c-74f3-bbc7-8cfd0600a299"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1994633, Requested 21072. Please try again in 471ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_857\\\",\\n       SUM(CAST(\\\"word_freq_telnet\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_telnet\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"word_freq_telnet\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_857\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_857\\\", and measure_col=\\\"word_freq_telnet\\\". Cast the measure from TEXT to REAL because the schema stores numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":12032,"output_tokens":535,"reasoning_output_tokens":350}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..367975bd81a79505c650d5a699034cad8605a361
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_857\",\n       SUM(CAST(\"word_freq_telnet\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_telnet\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\"word_freq_telnet\" AS REAL))) OVER (PARTITION BY \"class\"), 0) AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_857\"\nORDER BY share_within_group DESC;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_857\", and measure_col=\"word_freq_telnet\". Cast the measure from TEXT to REAL because the schema stores numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_62065963b9f8c10c/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..18564b8cdc987e6a2a901c13f6bd0c5ae3334c0d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-5794-7c43-b77b-c4845aa58923"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47358a7ae747c4ddb4a2f512b39dad1d4e9c9ccc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-686f-7962-a2a5-aa918049a087"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"word_freq_make\\\" AS REAL) <= 0.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"n1\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template using measure_col=\\\"word_freq_make\\\" and measure_threshold=0.0. CAST to REAL is used because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20306,"cached_input_tokens":12032,"output_tokens":237,"reasoning_output_tokens":132}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2047dc13fe2e2b15e3b24a0fd8c5a13a5fe76e5f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29913, "bytes_utf8": 29913, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 671, "bytes_utf8": 671, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 498, "reasoning_output_tokens": 322}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..30878948c4d1b478b933626cc35002f4fa1ccfb0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_6a0e1ca04380505b",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 498,
+    "total_tokens": 20951,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13602.88,
+    "sql_execution_elapsed_ms_total": 3.89,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f27b1172bd805fb82c40cddbece78564410e8bb5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:48:57.089790+00:00",
+  "ended_at": "2026-05-19T15:49:10.692693+00:00",
+  "elapsed_ms": 13602.88,
+  "prompt_metrics": {
+    "chars": 29913,
+    "bytes_utf8": 29913,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1042,
+    "bytes_utf8": 1042,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 671,
+      "bytes_utf8": 671,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20453,
+      "cached_input_tokens": 19840,
+      "output_tokens": 498,
+      "reasoning_output_tokens": 322
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..43d9ee29579dd6ea2475e8ffa345a33a51c2e58e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_your.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_your",
+    "top_k": 17,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.94,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..280ed1cc0d5fea17f881a24be4f3f41df38e4496
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ed-0b90-7102-a750-a5eedc29cd8e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_your\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_your\\\". CAST(\\\"word_freq_your\\\" AS REAL) is used because the schema snapshot stores numeric feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":498,"reasoning_output_tokens":322}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a375b3eab0b0c609117201bef82919199e58190a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_your\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_your\". CAST(\"word_freq_your\" AS REAL) is used because the schema snapshot stores numeric feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6a0e1ca04380505b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b920cd72acd2ed9a2d4a08b051f51bd55c53d22b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29439, "bytes_utf8": 29439, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 421, "bytes_utf8": 421, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 12032, "output_tokens": 282, "reasoning_output_tokens": 172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b752acbdd2888dfd817e084fa05c302405d08deb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_6d7fd78445cc65e8",
+    "api_calls": 0,
+    "input_tokens": 20330,
+    "cached_input_tokens": 12032,
+    "output_tokens": 282,
+    "total_tokens": 20612,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14749.43,
+    "sql_execution_elapsed_ms_total": 11.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..109e19b658c4cacc28e21ee96d091a03d826d0da
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:41:26.752838+00:00",
+  "ended_at": "2026-05-19T15:41:41.502294+00:00",
+  "elapsed_ms": 14749.43,
+  "prompt_metrics": {
+    "chars": 29439,
+    "bytes_utf8": 29439,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1116,
+    "bytes_utf8": 1116,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 421,
+      "bytes_utf8": 421,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20330,
+      "cached_input_tokens": 12032,
+      "output_tokens": 282,
+      "reasoning_output_tokens": 172
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7587565d67eec79c0816d0a13e48c7cd4df612f8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%3B.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%3B",
+    "top_k": 18,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..08fcb710154d81c97977964c6697606988812a8e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e6-2c58-7912-b5b1-94e78371b4c5"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20965. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%3B\\\". Cast the measure to REAL because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20330,"cached_input_tokens":12032,"output_tokens":282,"reasoning_output_tokens":172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea7d70e6789d9ddd4a66592d2a0b5dc886d9d96f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%3B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;","notes":"Applied the provided Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%3B\". Cast the measure to REAL because the schema stores feature columns as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..56ed0216fdbbcc5a8fccc1b3ae1d74b19be6c4eb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%3B.
+Result preview: [{"class": "0", "avg_measure": 0.050280846484935436}, {"class": "1", "avg_measure": 0.020573083287369003}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..49faa34d19d29039a59cf79c24b733619188ca6b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_6d7fd78445cc65e8
+-- problem_id: v2p_n1_0d954ee361568552
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "class",
+       AVG(CAST("char_freq_%3B" AS REAL)) OVER (PARTITION BY "class") AS "avg_measure"
+FROM "n1"
+ORDER BY "avg_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d5ee892d2f3578880a9ac88032b94bf1175d669
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%3B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.050280846484935436}, {\"class\": \"1\", \"avg_measure\": 0.020573083287369003}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 11.23}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1782014eb08ba18bbc29f583b84063ba3bb5f491
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:41:26.750960+00:00",
+  "ended_at": "2026-05-19T15:41:41.516911+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_6d7fd78445cc65e8",
+    "problem_id": "v2p_n1_0d954ee361568552",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%3B.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%3B",
+      "top_k": 18,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=2/2",
+      "binding_index=48"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_6d7fd78445cc65e8.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_6d7fd78445cc65e8",
+    "api_calls": 0,
+    "input_tokens": 20330,
+    "cached_input_tokens": 12032,
+    "output_tokens": 282,
+    "total_tokens": 20612,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14749.43,
+    "sql_execution_elapsed_ms_total": 11.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a06c7ac8630903cc2f3522699b58016d94734bf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:41:41.503355+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14749.43, "started_at": "2026-05-19T15:41:26.752838+00:00", "ended_at": "2026-05-19T15:41:41.502294+00:00", "prompt_metrics": {"chars": 29439, "bytes_utf8": 29439, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 421, "bytes_utf8": 421, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 12032, "output_tokens": 282, "reasoning_output_tokens": 172}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%3B\\\". Cast the measure to REAL because the schema stores feature columns as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..80f09e8af81fa72e55bd2ed06582cfd5026f996c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_6d7fd78445cc65e8",
+  "api_calls": 0,
+  "input_tokens": 20330,
+  "cached_input_tokens": 12032,
+  "output_tokens": 282,
+  "total_tokens": 20612,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 14749.43,
+  "sql_execution_elapsed_ms_total": 11.23,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6d7fd78445cc65e8/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..181c5b1dfc9a2f7acf1a0147e4897c3a72725ead
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29531, "bytes_utf8": 29531, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 545, "bytes_utf8": 545, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 424, "reasoning_output_tokens": 263}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e1cf69221ebcc469527c48ce68aaef9b7bc2c985
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_6e287feb5471b8cc",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 19840,
+    "output_tokens": 424,
+    "total_tokens": 20796,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9795.38,
+    "sql_execution_elapsed_ms_total": 6.45,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ff2259a3a37838b83d8ca2afbb77564348baa8e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:44:48.108958+00:00",
+  "ended_at": "2026-05-19T15:44:57.904368+00:00",
+  "elapsed_ms": 9795.38,
+  "prompt_metrics": {
+    "chars": 29531,
+    "bytes_utf8": 29531,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 924,
+    "bytes_utf8": 924,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 545,
+      "bytes_utf8": 545,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20372,
+      "cached_input_tokens": 19840,
+      "output_tokens": 424,
+      "reasoning_output_tokens": 263
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76fa9b9aaa0a8cfc04cda8a23b1a288933bc9c42
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_our.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "word_freq_our",
+    "top_k": 11,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.38,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de59d86150c3c500996c4347c1dac58d9903d619
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e9-3f00-7031-93a8-e1076b234922"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n    SELECT CAST(\\\"word_freq_our\\\" AS REAL) AS \\\"word_freq_our\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_our\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n    FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_our\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_our\\\" DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"word_freq_our\\\" and num_tiles fixed at 10. Cast to REAL because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":424,"reasoning_output_tokens":263}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78c9883717c870425426433e3eba84d988194d58
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n    SELECT CAST(\"word_freq_our\" AS REAL) AS \"word_freq_our\",\n           NTILE(10) OVER (ORDER BY CAST(\"word_freq_our\" AS REAL) DESC) AS \"tail_bucket\"\n    FROM \"n1\"\n)\nSELECT \"word_freq_our\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_our\" DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"word_freq_our\" and num_tiles fixed at 10. Cast to REAL because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6e287feb5471b8cc/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0092be3340d615a68f15949b9c7019b7c27e3513
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29764, "bytes_utf8": 29764, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 625, "bytes_utf8": 625, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20435, "cached_input_tokens": 12032, "output_tokens": 543, "reasoning_output_tokens": 369}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..86072c07e83c29ac363f66e2730b8b765c8b5d13
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_7223094c4be238f3",
+    "api_calls": 0,
+    "input_tokens": 20435,
+    "cached_input_tokens": 12032,
+    "output_tokens": 543,
+    "total_tokens": 20978,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12551.89,
+    "sql_execution_elapsed_ms_total": 4.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..06620dd53b0c796aee6127c44185c930fb9a8923
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:34:08.881387+00:00",
+  "ended_at": "2026-05-19T15:34:21.433314+00:00",
+  "elapsed_ms": 12551.89,
+  "prompt_metrics": {
+    "chars": 29764,
+    "bytes_utf8": 29764,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1005,
+    "bytes_utf8": 1005,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 625,
+      "bytes_utf8": 625,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20435,
+      "cached_input_tokens": 12032,
+      "output_tokens": 543,
+      "reasoning_output_tokens": 369
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7d2db4b429bb46090aff06b7833e09834a0f9f16
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_hp.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hp",
+    "item_col": "word_freq_hpl",
+    "top_k": 19,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a2fa3c4968d95a6b52464152685fb42072818cfa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40df-7dfe-76b1-9b49-3025b768eba1"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_hpl\\\",\\n       SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hp\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_hpl\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the required Within-Group Share of Total template with bound roles group_col=\\\"class\\\", item_col=\\\"word_freq_hpl\\\", and measure_col=\\\"word_freq_hp\\\". CAST to REAL is used because the schema stores these fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20435,"cached_input_tokens":12032,"output_tokens":543,"reasoning_output_tokens":369}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..23b8fafecc405bf02638d947d5f56fb68e7c973a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_hpl\",\n       SUM(CAST(\"word_freq_hp\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_hp\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_hp\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_hpl\"\nORDER BY share_within_group DESC;","notes":"Applied the required Within-Group Share of Total template with bound roles group_col=\"class\", item_col=\"word_freq_hpl\", and measure_col=\"word_freq_hp\". CAST to REAL is used because the schema stores these fields as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7223094c4be238f3/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..74cca00c41753e25ca911b68e5d645019d75e1b4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 408, "bytes_utf8": 408, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 12032, "output_tokens": 509, "reasoning_output_tokens": 403}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c627d0235182598c5a0ba4633514efa84809f1c1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_72764dd7d1f0dcdf",
+    "api_calls": 0,
+    "input_tokens": 20328,
+    "cached_input_tokens": 12032,
+    "output_tokens": 509,
+    "total_tokens": 20837,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11078.14,
+    "sql_execution_elapsed_ms_total": 19.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b4214888052c0102c58ad92af975003b2b31c6d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:43:42.739036+00:00",
+  "ended_at": "2026-05-19T15:43:53.817218+00:00",
+  "elapsed_ms": 11078.14,
+  "prompt_metrics": {
+    "chars": 29440,
+    "bytes_utf8": 29440,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 763,
+    "bytes_utf8": 763,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 408,
+      "bytes_utf8": 408,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20328,
+      "cached_input_tokens": 12032,
+      "output_tokens": 509,
+      "reasoning_output_tokens": 403
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8bce09a357d59104f623f27f8cc5a2f77bb98b36
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%23.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%23",
+    "top_k": 18,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8fdc957ae6b8f6ab6a62be090ddb84e56c740825
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e8-3f9b-7961-84b0-cdf96fd9b803"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%23\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%23\\\". Cast the measure to REAL because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20328,"cached_input_tokens":12032,"output_tokens":509,"reasoning_output_tokens":403}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f8763cc7c04d5fa39d58c06bf0d20b6e07ea099d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%23\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Used the provided Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%23\". Cast the measure to REAL because the schema stores feature columns as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a0b2d6cede0bee366ff73bb5b7605651d19583c1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%23.
+Result preview: [{"class": "1", "avg_measure": 0.07887699944842802}, {"class": "0", "avg_measure": 0.021713055954088952}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..60ec1bf7e0878b242ebc9b0c0d6feff9ead3a0c9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_72764dd7d1f0dcdf
+-- problem_id: v2p_n1_afcffb2c63bba231
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("char_freq_%23" AS REAL)) OVER (PARTITION BY "class") AS avg_measure
+FROM "n1"
+ORDER BY avg_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..238943259b1f651e223db26c4f1eab6545fa6761
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%23\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%23\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"1\", \"avg_measure\": 0.07887699944842802}, {\"class\": \"0\", \"avg_measure\": 0.021713055954088952}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 19.32}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..0cea02b43fe564aafbf23706f2bda49baac320be
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:43:42.735371+00:00",
+  "ended_at": "2026-05-19T15:43:53.841601+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_72764dd7d1f0dcdf",
+    "problem_id": "v2p_n1_afcffb2c63bba231",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%23.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%23",
+      "top_k": 18,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=6",
+      "sql_variant_index=2/2",
+      "binding_index=53"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_72764dd7d1f0dcdf.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_72764dd7d1f0dcdf",
+    "api_calls": 0,
+    "input_tokens": 20328,
+    "cached_input_tokens": 12032,
+    "output_tokens": 509,
+    "total_tokens": 20837,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11078.14,
+    "sql_execution_elapsed_ms_total": 19.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57198de0f1c156ea92d6359e5f3d38eae94feb33
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:43:53.818866+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11078.14, "started_at": "2026-05-19T15:43:42.739036+00:00", "ended_at": "2026-05-19T15:43:53.817218+00:00", "prompt_metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 408, "bytes_utf8": 408, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 12032, "output_tokens": 509, "reasoning_output_tokens": 403}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%23\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%23\\\". Cast the measure to REAL because the schema stores feature columns as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..f559e84b87e342861362d1e01d7ffc177e3cc2dc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_72764dd7d1f0dcdf",
+  "api_calls": 0,
+  "input_tokens": 20328,
+  "cached_input_tokens": 12032,
+  "output_tokens": 509,
+  "total_tokens": 20837,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11078.14,
+  "sql_execution_elapsed_ms_total": 19.32,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_72764dd7d1f0dcdf/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..7881b271d392d3a6a84f1b6491b9988c71100fb2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:42.971002+00:00",
+  "ended_at": "2026-05-19T16:00:45.835176+00:00",
+  "elapsed_ms": 2864.13,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29297,
+    "bytes_utf8": 29297,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e6a8f70b9aa53ff4fac2aaeddefa88f44d79f48
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:46.838666+00:00",
+  "ended_at": "2026-05-19T16:00:50.233208+00:00",
+  "elapsed_ms": 3394.5,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29297,
+    "bytes_utf8": 29297,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d5e6cfe6c770fec7799bc621c9be900eb050e158
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%23.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "char_freq_%23",
+    "top_k": 10,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d5e6cfe6c770fec7799bc621c9be900eb050e158
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%23.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "char_freq_%23",
+    "top_k": 10,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..559944d3ee2e1c2840a894b3e74a2b31254e1939
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-d0e6-7450-b4a7-14798e2f00d1"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..44d3418c8b090aec5c6e08c18e58f9f038808603
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-d0e6-7450-b4a7-14798e2f00d1"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1db5b539a765d45dc27144862539c2b6dba16507
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-dffd-73c0-8140-6dbab0872f74"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5482a114df7222058ef108625f2e738d1118eada
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-dffd-73c0-8140-6dbab0872f74"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..da1d7a6fb2bab021fe7e761d2abc0627532fe5e9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/run_manifest.json
@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:00:42.966904+00:00",
+  "ended_at": "2026-05-19T16:00:50.234087+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_736255ac838f92cc",
+    "problem_id": "v2p_n1_352ddc834469c23b",
+    "dataset_id": "n1",
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%23.",
+    "bindings": {
+      "measure_col": "char_freq_%23",
+      "top_k": 10,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/1",
+      "binding_index=110"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 10,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2295b3e3eb7854965a25d1cdd1bfb709defdefed
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_736255ac838f92cc/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:00:45.836369+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2864.13, "started_at": "2026-05-19T16:00:42.971002+00:00", "ended_at": "2026-05-19T16:00:45.835176+00:00", "prompt_metrics": {"chars": 29297, "bytes_utf8": 29297, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f7-d0e6-7450-b4a7-14798e2f00d1\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:00:50.234001+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3394.5, "started_at": "2026-05-19T16:00:46.838666+00:00", "ended_at": "2026-05-19T16:00:50.233208+00:00", "prompt_metrics": {"chars": 29297, "bytes_utf8": 29297, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f7-dffd-73c0-8140-6dbab0872f74\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b860b61e4457416d8cd2ff570c504afbf5f26cf6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29364, "bytes_utf8": 29364, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 390, "bytes_utf8": 390, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 291, "reasoning_output_tokens": 188}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..27db82e0870427fe42be3008e8d377c8d7c7b88b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_74df09725c79c400",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 291,
+    "total_tokens": 20606,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8455.43,
+    "sql_execution_elapsed_ms_total": 2.98,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a0be920857dac50dd9348bb0dded7d8d24ff4f5f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:47.514188+00:00",
+  "ended_at": "2026-05-19T15:28:55.969650+00:00",
+  "elapsed_ms": 8455.43,
+  "prompt_metrics": {
+    "chars": 29364,
+    "bytes_utf8": 29364,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 752,
+    "bytes_utf8": 752,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 390,
+      "bytes_utf8": 390,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20315,
+      "cached_input_tokens": 12032,
+      "output_tokens": 291,
+      "reasoning_output_tokens": 188
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b91ce23bd565564fb66336d729e6daeb7a59be07
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_address.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_address",
+    "top_k": 16,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6182b7c35b8a28ecf6f2fd6a157bb0c5d57389c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-969f-7631-bbca-725d245d0e3c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_address\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the provided Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_address\\\". Cast the measure column to REAL because the schema snapshot types it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":291,"reasoning_output_tokens":188}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b33b8136f413f62e5a3e8fbbc60970edfb185bff
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_address\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Used the provided Grouped Numeric Sum template with group_col=\"class\" and measure_col=\"word_freq_address\". Cast the measure column to REAL because the schema snapshot types it as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd99c5b84888ebf256b1749838ac11e1de7430a1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_address.
+Result preview: [{"class": "0", "total_measure": 681.5699999999999}, {"class": "1", "total_measure": 298.51}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..31cab7b408e5dd3d93053c6cf21e66b46bac61d4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_74df09725c79c400
+-- problem_id: v2p_n1_b5e01ab957ee4759
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_address" AS REAL)) AS "total_measure"
+FROM "n1"
+GROUP BY "class"
+ORDER BY "total_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4fc0b8c3b512edf579512432076e5c5df8e7b94e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_address\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_address\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"0\", \"total_measure\": 681.5699999999999}, {\"class\": \"1\", \"total_measure\": 298.51}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.98}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..39aeee69ab2ca34e14ddeeb7c22b32b9765cf192
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:28:47.511521+00:00",
+  "ended_at": "2026-05-19T15:28:55.975325+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_74df09725c79c400",
+    "problem_id": "v2p_n1_b5e01ab957ee4759",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_conditional_contrast",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_address.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_address",
+      "top_k": 16,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=2/2",
+      "binding_index=1"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_74df09725c79c400.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_74df09725c79c400",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 291,
+    "total_tokens": 20606,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8455.43,
+    "sql_execution_elapsed_ms_total": 2.98,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd1a03fd5c0b2ce23c239efc197ba8d32dfc9d32
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:28:55.970694+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8455.43, "started_at": "2026-05-19T15:28:47.514188+00:00", "ended_at": "2026-05-19T15:28:55.969650+00:00", "prompt_metrics": {"chars": 29364, "bytes_utf8": 29364, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 390, "bytes_utf8": 390, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 291, "reasoning_output_tokens": 188}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_address\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the provided Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_address\\\". Cast the measure column to REAL because the schema snapshot types it as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..a724496f06d2d5c52ee53d42ea4caa45e936cfd4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_74df09725c79c400",
+  "api_calls": 0,
+  "input_tokens": 20315,
+  "cached_input_tokens": 12032,
+  "output_tokens": 291,
+  "total_tokens": 20606,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 8455.43,
+  "sql_execution_elapsed_ms_total": 2.98,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_74df09725c79c400/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c0ec86b357aeb2fc711d6c8954029b5d4d614f5a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29764, "bytes_utf8": 29764, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 616, "bytes_utf8": 616, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 19840, "output_tokens": 611, "reasoning_output_tokens": 434}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..08475af7d52838e3e94cde12477443348d69fda9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_764043fc2ec3d271",
+    "api_calls": 0,
+    "input_tokens": 20436,
+    "cached_input_tokens": 19840,
+    "output_tokens": 611,
+    "total_tokens": 21047,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14463.62,
+    "sql_execution_elapsed_ms_total": 7.29,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..91809b32949e2c9642d9bbcce826015eee258f43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:35:20.552134+00:00",
+  "ended_at": "2026-05-19T15:35:35.015783+00:00",
+  "elapsed_ms": 14463.62,
+  "prompt_metrics": {
+    "chars": 29764,
+    "bytes_utf8": 29764,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 998,
+    "bytes_utf8": 998,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 616,
+      "bytes_utf8": 616,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20436,
+      "cached_input_tokens": 19840,
+      "output_tokens": 611,
+      "reasoning_output_tokens": 434
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0d6d41c18b4db30f004abff3559d9c4cf6b4f206
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_650.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_650",
+    "item_col": "word_freq_lab",
+    "top_k": 12,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2f4bbe57d0d172c7d17ca631d2ba38e416a1cf1e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e0-95fc-78d1-a9b3-8b303253efdc"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_lab\\\",\\n  SUM(CAST(\\\"word_freq_650\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_650\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_650\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_lab\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_lab\\\", and measure_col=\\\"word_freq_650\\\". CAST to REAL is included because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20436,"cached_input_tokens":19840,"output_tokens":611,"reasoning_output_tokens":434}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e147e85e7c10f4d05cfe51d92317ff5e5e6853b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_lab\",\n  SUM(CAST(\"word_freq_650\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_650\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_650\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_lab\"\nORDER BY share_within_group DESC;","notes":"Used the planned Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_lab\", and measure_col=\"word_freq_650\". CAST to REAL is included because the schema stores feature columns as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_764043fc2ec3d271/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12a267daf6bb79b93e4578fb02d7b30d8a6878b9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29435, "bytes_utf8": 29435, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 399, "bytes_utf8": 399, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20327, "cached_input_tokens": 12032, "output_tokens": 211, "reasoning_output_tokens": 108}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..817c355dae3d538dc4b3b8193c1d710053037e54
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_79278ef620cacf90",
+    "api_calls": 0,
+    "input_tokens": 20327,
+    "cached_input_tokens": 12032,
+    "output_tokens": 211,
+    "total_tokens": 20538,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10332.44,
+    "sql_execution_elapsed_ms_total": 14.54,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a30f08faffa48e1322941d2a0c1dc1ac0af80d1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:43:32.383622+00:00",
+  "ended_at": "2026-05-19T15:43:42.716093+00:00",
+  "elapsed_ms": 10332.44,
+  "prompt_metrics": {
+    "chars": 29435,
+    "bytes_utf8": 29435,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 753,
+    "bytes_utf8": 753,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 399,
+      "bytes_utf8": 399,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20327,
+      "cached_input_tokens": 12032,
+      "output_tokens": 211,
+      "reasoning_output_tokens": 108
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d16458b448443aee95639ee6b4186a99c55df166
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%23.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%23",
+    "top_k": 13,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b13919af53f0aef244bebbe87e3dad801f77d4c9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e8-173c-7f62-b06c-49be1e796798"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%23\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%23\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20327,"cached_input_tokens":12032,"output_tokens":211,"reasoning_output_tokens":108}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3d05ae988407ea93d6cae6184fbd25a821d26955
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%23\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%23\". CAST to REAL is used because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_79278ef620cacf90/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d4b64e1edb5df76afb824686f361c463ba2d5c5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29529, "bytes_utf8": 29529, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 530, "bytes_utf8": 530, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20374, "cached_input_tokens": 19840, "output_tokens": 457, "reasoning_output_tokens": 292}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..32b3d4ab96875ea49c98987f02fa90a8f307a403
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_7a856aa9125fcb9d",
+    "api_calls": 0,
+    "input_tokens": 20374,
+    "cached_input_tokens": 19840,
+    "output_tokens": 457,
+    "total_tokens": 20831,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10989.34,
+    "sql_execution_elapsed_ms_total": 7.1,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..00fef665eed37c50ef0d5488224c12f2a7c0f037
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:44:37.106775+00:00",
+  "ended_at": "2026-05-19T15:44:48.096148+00:00",
+  "elapsed_ms": 10989.34,
+  "prompt_metrics": {
+    "chars": 29529,
+    "bytes_utf8": 29529,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 909,
+    "bytes_utf8": 909,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 530,
+      "bytes_utf8": 530,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20374,
+      "cached_input_tokens": 19840,
+      "output_tokens": 457,
+      "reasoning_output_tokens": 292
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..acedc72d8838ed555ffb28054fcd33e89a59262e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_3d.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "word_freq_3d",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..589b0f9a5b22a3f86a28ed527e4f155ed04fb8c5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e9-1402-7642-bf11-a63ea9001e8b"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n    SELECT CAST(\\\"word_freq_3d\\\" AS REAL) AS \\\"word_freq_3d\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_3d\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n    FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_3d\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_3d\\\" DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"word_freq_3d\\\" and num_tiles=10. Cast to REAL because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20374,"cached_input_tokens":19840,"output_tokens":457,"reasoning_output_tokens":292}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8fe8a192d9b1858c283bdee5b0181ece2cfa3989
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n    SELECT CAST(\"word_freq_3d\" AS REAL) AS \"word_freq_3d\",\n           NTILE(10) OVER (ORDER BY CAST(\"word_freq_3d\" AS REAL) DESC) AS \"tail_bucket\"\n    FROM \"n1\"\n)\nSELECT \"word_freq_3d\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_3d\" DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"word_freq_3d\" and num_tiles=10. Cast to REAL because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7a856aa9125fcb9d/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59ddea36888db85213f5c68a149575649a4f5f03
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30144, "bytes_utf8": 30144, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 762, "bytes_utf8": 762, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 12032, "output_tokens": 505, "reasoning_output_tokens": 300}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ce57b0d8b8b18f67802503f8c75a2c866ec1b81a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_7e1b9d86b89ed8e1",
+    "api_calls": 0,
+    "input_tokens": 20520,
+    "cached_input_tokens": 12032,
+    "output_tokens": 505,
+    "total_tokens": 21025,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13350.36,
+    "sql_execution_elapsed_ms_total": 5.29,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8b5fc178e3bb06e83eea1d6e0fd7bcf8a9d0b2e4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:40:16.090429+00:00",
+  "ended_at": "2026-05-19T15:40:29.440838+00:00",
+  "elapsed_ms": 13350.36,
+  "prompt_metrics": {
+    "chars": 30144,
+    "bytes_utf8": 30144,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1127,
+    "bytes_utf8": 1127,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 762,
+      "bytes_utf8": 762,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20520,
+      "cached_input_tokens": 12032,
+      "output_tokens": 505,
+      "reasoning_output_tokens": 300
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1340de90bac3c78389495ee043b58138b214788f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_ratio_two_conditions",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d5d644cb4ea664b3d7e11f33ca6fae0d87410291
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e5-1855-7991-8d0a-855fd57c293e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"class\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the provided grouped-ratio template with both group_col and condition_col bound to \\\"class\\\". Because the grouping column and condition column are the same, the '0' class group will have a NULL ratio due to zero '1' rows within that group, while the '1' class group will yield 0.0.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":12032,"output_tokens":505,"reasoning_output_tokens":300}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..24f95aa96a0e1de5900af721c263e90611f59ff8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n    SELECT \"class\",\n           SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT \"class\",\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Applied the provided grouped-ratio template with both group_col and condition_col bound to \"class\". Because the grouping column and condition column are the same, the '0' class group will have a NULL ratio due to zero '1' rows within that group, while the '1' class group will yield 0.0."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7e1b9d86b89ed8e1/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4daf6a320b0d1fc83e2e02856a3f025a3f790587
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29437, "bytes_utf8": 29437, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 433, "bytes_utf8": 433, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20327, "cached_input_tokens": 19840, "output_tokens": 462, "reasoning_output_tokens": 352}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..11337588dabd3fb0122189a97f014c7b7632bcd2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_8085e0cd33f2116b",
+    "api_calls": 0,
+    "input_tokens": 20327,
+    "cached_input_tokens": 19840,
+    "output_tokens": 462,
+    "total_tokens": 20789,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10207.65,
+    "sql_execution_elapsed_ms_total": 20.39,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..05d61b2662dbc0e92f5caeadfc2858a4f1bff886
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:42:41.591003+00:00",
+  "ended_at": "2026-05-19T15:42:51.798683+00:00",
+  "elapsed_ms": 10207.65,
+  "prompt_metrics": {
+    "chars": 29437,
+    "bytes_utf8": 29437,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 787,
+    "bytes_utf8": 787,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 433,
+      "bytes_utf8": 433,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20327,
+      "cached_input_tokens": 19840,
+      "output_tokens": 462,
+      "reasoning_output_tokens": 352
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4119b56cc6778622099d71ad7f895660d37b34ad
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%21.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%21",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.315,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..89a4a7b1b2130e248c1e010fc01b548ddaee9a94
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e7-50b0-7e52-a7e6-73460439e413"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%21\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested window-partition-average template with \\\"class\\\" as the partitioning group and \\\"char_freq_%21\\\" as the numeric measure; CAST to REAL is needed because the schema stores these values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20327,"cached_input_tokens":19840,"output_tokens":462,"reasoning_output_tokens":352}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c35027a74e7cbd33d600c64d24a2539ef6c83b0e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%21\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Uses the requested window-partition-average template with \"class\" as the partitioning group and \"char_freq_%21\" as the numeric measure; CAST to REAL is needed because the schema stores these values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8085e0cd33f2116b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f2e821a9848905b5b6407da193e441feda12264
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_money.
+Result preview: [{"class": "1", "group_value": 385.95}, {"class": "0", "group_value": 47.78}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..a616c9082235c489ee5b94705554b194fe0c9048
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_855c79c74e5ae2c5
+-- problem_id: v2p_n1_702df9a07a37bfd8
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_money" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5894effe2c46d1ea4361bd0a3e3c46f77283560
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 385.95}, {\"class\": \"0\", \"group_value\": 47.78}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.16}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..d99875e9bfeb12d8e9034bd73d34744e9ff2a3fa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:50:35.776204+00:00",
+  "ended_at": "2026-05-19T15:50:52.983627+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_855c79c74e5ae2c5",
+    "problem_id": "v2p_n1_702df9a07a37bfd8",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_money.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_money",
+      "top_k": 15,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=9",
+      "sql_variant_index=2/2",
+      "binding_index=80"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 9,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_855c79c74e5ae2c5.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_855c79c74e5ae2c5",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 12032,
+    "output_tokens": 676,
+    "total_tokens": 21129,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 17197.7,
+    "sql_execution_elapsed_ms_total": 4.16,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2151215071d414d25dced331ebee741d956dd72a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:50:52.977266+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17197.7, "started_at": "2026-05-19T15:50:35.778318+00:00", "ended_at": "2026-05-19T15:50:52.976050+00:00", "prompt_metrics": {"chars": 29915, "bytes_utf8": 29915, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 610, "bytes_utf8": 610, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 12032, "output_tokens": 676, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the requested template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_money\\\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5b3aab019b4447c5158a96f05b1c96783a86193
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_855c79c74e5ae2c5",
+  "api_calls": 0,
+  "input_tokens": 20453,
+  "cached_input_tokens": 12032,
+  "output_tokens": 676,
+  "total_tokens": 21129,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 17197.7,
+  "sql_execution_elapsed_ms_total": 4.16,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae9580a2d2eae6a1a28691d87d965a74b27ad85f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 419, "bytes_utf8": 419, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 284, "reasoning_output_tokens": 172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..590a0f9eca96eeceb22d06f328714ebb2d35814f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_86a8c021ed168183",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 19840,
+    "output_tokens": 284,
+    "total_tokens": 20656,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11813.07,
+    "sql_execution_elapsed_ms_total": 3.68,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d06d67e0be141ce1aeb65b2e35eea83e4af4f73
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:59:24.531463+00:00",
+  "ended_at": "2026-05-19T15:59:36.344560+00:00",
+  "elapsed_ms": 11813.07,
+  "prompt_metrics": {
+    "chars": 29585,
+    "bytes_utf8": 29585,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 774,
+    "bytes_utf8": 774,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 419,
+      "bytes_utf8": 419,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20372,
+      "cached_input_tokens": 19840,
+      "output_tokens": 284,
+      "reasoning_output_tokens": 172
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b57cfc4593c62dece5ee6fa24f32f74c92a8bfc5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
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+      "capital_run_length_longest": "485",
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+    },
+    {
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+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
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+    {
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+      "word_freq_re": "0",
+      "word_freq_edu": "0",
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+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
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+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 15,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3e518096f9118b24bf15f706318c327468cdb1c9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-9e6f-7110-acb8-77644305b21e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the required Grouped Condition Rate template with both group_col and condition_col bound to \\\"class\\\". Used '1' as a text literal because the schema snapshot shows \\\"class\\\" stored as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":284,"reasoning_output_tokens":172}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e59b55bc1f0c6eb0de758e299a66f35509afd462
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Applied the required Grouped Condition Rate template with both group_col and condition_col bound to \"class\". Used '1' as a text literal because the schema snapshot shows \"class\" stored as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_86a8c021ed168183/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..206986c2bdf9c40a1365333635a3bef85c6b6173
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/run_manifest.json
@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:02:46.835714+00:00",
+  "ended_at": "2026-05-19T16:02:54.000036+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_8d4076cdf75ee6c9",
+    "problem_id": "v2p_n1_e7c380e613d99dbe",
+    "dataset_id": "n1",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "top_k": 18,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.16,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=2/2",
+      "binding_index=123"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2cc2ce1fe3dc3fe1840c01024504b463d786356a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:02:49.958232+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3119.17, "started_at": "2026-05-19T16:02:46.838285+00:00", "ended_at": "2026-05-19T16:02:49.957485+00:00", "prompt_metrics": {"chars": 29336, "bytes_utf8": 29336, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f9-b4be-7901-8f1d-c41db6f1bb04\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:02:53.999842+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3038.44, "started_at": "2026-05-19T16:02:50.959486+00:00", "ended_at": "2026-05-19T16:02:53.997966+00:00", "prompt_metrics": {"chars": 29336, "bytes_utf8": 29336, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f9-c4e5-7ca0-8646-8b1c25a9530c\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..000e89a264c135cc29b6e4598bb0ec19dee647b9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 19840, "output_tokens": 348, "reasoning_output_tokens": 236}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d320f5f1e2bdc0ae062637a63b6e88d26147b6fb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_904381230d6ea03b",
+    "api_calls": 0,
+    "input_tokens": 20373,
+    "cached_input_tokens": 19840,
+    "output_tokens": 348,
+    "total_tokens": 20721,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12791.69,
+    "sql_execution_elapsed_ms_total": 1.98,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8c464cc62b0834aee16f632cda7491501274105
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:57:49.314058+00:00",
+  "ended_at": "2026-05-19T15:57:52.148857+00:00",
+  "elapsed_ms": 2834.77,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29588,
+    "bytes_utf8": 29588,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..dcca3d512dbd619a20404a7d09a77a8104413dbd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:57:53.151847+00:00",
+  "ended_at": "2026-05-19T15:58:03.108803+00:00",
+  "elapsed_ms": 9956.92,
+  "prompt_metrics": {
+    "chars": 29588,
+    "bytes_utf8": 29588,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 775,
+    "bytes_utf8": 775,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 416,
+      "bytes_utf8": 416,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20373,
+      "cached_input_tokens": 19840,
+      "output_tokens": 348,
+      "reasoning_output_tokens": 236
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a9aa056baf334699a1b828d9a2be2c91b03a96c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a9aa056baf334699a1b828d9a2be2c91b03a96c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..834ec6bbf86d708d798d533ee6701acc6176922a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-2a86-7ec0-815e-3cbca9ee1127"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db5d7f0d595318074e839ad03fc5c49040d24ee0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-2a86-7ec0-815e-3cbca9ee1127"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a5ee16aefef1c8db395814aa8dce74d26082eb30
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-396c-7671-be12-e4b8a48f6a7c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the requested Grouped Condition Rate template with both group and condition bound to \\\"class\\\". This yields the within-class proportion of rows where class='0' for each class value.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20373,"cached_input_tokens":19840,"output_tokens":348,"reasoning_output_tokens":236}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76945dae939761c94dd2c6c6f83155d6495407a4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the requested Grouped Condition Rate template with both group and condition bound to \"class\". This yields the within-class proportion of rows where class='0' for each class value."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_904381230d6ea03b/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..617d49262d9ac2a42d50c4ebf43a115599cd00c8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:58:20.805281+00:00",
+  "ended_at": "2026-05-19T15:58:24.231511+00:00",
+  "elapsed_ms": 3426.19,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29581,
+    "bytes_utf8": 29581,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..47f38e04812e62057f6b476f068329896aafe019
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:58:25.234455+00:00",
+  "ended_at": "2026-05-19T15:58:28.150293+00:00",
+  "elapsed_ms": 2915.79,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29581,
+    "bytes_utf8": 29581,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a70f543cc15c7890458aa99ec9f818ec229e201d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 17,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a70f543cc15c7890458aa99ec9f818ec229e201d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 17,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5623efac928f18c94f86c907c1751a236d4571f3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-a582-7fd3-8c93-ba2f0e23e39c"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aaabd0a264d98e836d26f4625d186b52cbeac12b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-a582-7fd3-8c93-ba2f0e23e39c"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ea4de412e36ab9c1b4d6bb22171a91c4b4b5559
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-b6ee-7db3-a301-75cfe2161416"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa208e20f4e1250fb37f461dc35ebee601f03521
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-b6ee-7db3-a301-75cfe2161416"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9356e7b5db5179e8/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..93ce4951354bedfc07881c942281ed8ad1c18a44
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_internet.
+Result preview: [{"class": "1", "total_measure": 377.36}, {"class": "0", "total_measure": 107.1}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c061f8402f2137805fb7f05f0345aa383dd22da4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_947c549aff46407a
+-- problem_id: v2p_n1_3fccca7e7196b754
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_internet" AS REAL)) AS "total_measure"
+FROM "n1"
+GROUP BY "class"
+ORDER BY "total_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7451f1446faaff7ff3a940c87f51424501faea7c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_internet\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_internet\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 377.36}, {\"class\": \"0\", \"total_measure\": 107.1}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.02}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..b74cd633add707bb665b54e291eb47f7edc93489
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:32:03.022644+00:00",
+  "ended_at": "2026-05-19T15:32:12.113387+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_947c549aff46407a",
+    "problem_id": "v2p_n1_3fccca7e7196b754",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_conditional_contrast",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_internet.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_internet",
+      "top_k": 17,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=7"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_947c549aff46407a.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_947c549aff46407a",
+    "api_calls": 0,
+    "input_tokens": 20317,
+    "cached_input_tokens": 12032,
+    "output_tokens": 308,
+    "total_tokens": 20625,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9077.15,
+    "sql_execution_elapsed_ms_total": 5.02,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7e9e9806c5564f5d21c1b15c0e0a47cf5e23ae83
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:32:12.105655+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9077.15, "started_at": "2026-05-19T15:32:03.026892+00:00", "ended_at": "2026-05-19T15:32:12.104092+00:00", "prompt_metrics": {"chars": 29366, "bytes_utf8": 29366, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 377, "bytes_utf8": 377, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20317, "cached_input_tokens": 12032, "output_tokens": 308, "reasoning_output_tokens": 207}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_internet\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the grouped numeric sum template with \\\"class\\\" as the grouping column and cast \\\"word_freq_internet\\\" to REAL because the schema snapshot shows it is stored as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..310be1e8e82a6b83c1e313578bf931c063137e44
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_947c549aff46407a",
+  "api_calls": 0,
+  "input_tokens": 20317,
+  "cached_input_tokens": 12032,
+  "output_tokens": 308,
+  "total_tokens": 20625,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 9077.15,
+  "sql_execution_elapsed_ms_total": 5.02,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7863a7f2b2b4165941ff9cad71d9c409f62f846
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29253, "bytes_utf8": 29253, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 268, "bytes_utf8": 268, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 202, "reasoning_output_tokens": 130}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d059eea404d425e48b3a125722606b4a5d266de
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_94c3200020f54d91",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 19840,
+    "output_tokens": 202,
+    "total_tokens": 20488,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15587.4,
+    "sql_execution_elapsed_ms_total": 3.18,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..581a6a7559f86b791c02029ff93f93e8f899b39e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:32:20.927028+00:00",
+  "ended_at": "2026-05-19T15:32:36.514463+00:00",
+  "elapsed_ms": 15587.4,
+  "prompt_metrics": {
+    "chars": 29253,
+    "bytes_utf8": 29253,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1287,
+    "bytes_utf8": 1287,
+    "lines": 6,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 268,
+      "bytes_utf8": 268,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20286,
+      "cached_input_tokens": 19840,
+      "output_tokens": 202,
+      "reasoning_output_tokens": 130
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa6aac5f9f23c17388db095e9fc8a8160202c387
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 13,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..726852200c40a56e6af942a1b4a226936e20e0b6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,6 @@
+{"type":"thread.started","thread_id":"019e40dd-d845-7962-b174-cd4cf60b6494"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1979822, Requested 20921. Please try again in 22ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Grouped count by the bound category column \\\"class\\\" to inspect class subgroup size distribution.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":19840,"output_tokens":202,"reasoning_output_tokens":130}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..83f6d94ff8f80c0336cfe0893ab424007cf6fce2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;","notes":"Grouped count by the bound category column \"class\" to inspect class subgroup size distribution."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_94c3200020f54d91/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df4b25ec6378e469c91eace7404bd109302048d4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29526, "bytes_utf8": 29526, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 480, "bytes_utf8": 480, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20360, "cached_input_tokens": 12032, "output_tokens": 344, "reasoning_output_tokens": 217}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4c78815f4e7e2320c94c1836f73d3fea84383c30
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_98994cae062c7a2c",
+    "api_calls": 0,
+    "input_tokens": 20360,
+    "cached_input_tokens": 12032,
+    "output_tokens": 344,
+    "total_tokens": 20704,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8707.98,
+    "sql_execution_elapsed_ms_total": 5.4,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ef5b6e3972ee158c51cabde26d494822690c38b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:03:59.779368+00:00",
+  "ended_at": "2026-05-19T16:04:08.487384+00:00",
+  "elapsed_ms": 8707.98,
+  "prompt_metrics": {
+    "chars": 29526,
+    "bytes_utf8": 29526,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 840,
+    "bytes_utf8": 840,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 480,
+      "bytes_utf8": 480,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20360,
+      "cached_input_tokens": 12032,
+      "output_tokens": 344,
+      "reasoning_output_tokens": 217
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..792c0f328d6ee1d849b3325840c915c65cc847aa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_you.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_you",
+    "top_k": 17,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 2.14,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e2cd4cd29eadee23876bf02f9fee973a94f8fd5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-d1a6-7a53-8cbc-edf03b5f7759"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.14\\nORDER BY total_measure DESC\\nLIMIT 17;\",\"notes\":\"Used the required thresholded group ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20360,"cached_input_tokens":12032,"output_tokens":344,"reasoning_output_tokens":217}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f54fcfd71f5502bc7777ef1d55fb80ffdc96dd05
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_you\" AS REAL)) > 2.14\nORDER BY total_measure DESC\nLIMIT 17;","notes":"Used the required thresholded group ranking template with group_col=\"class\" and measure_col=\"word_freq_you\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_98994cae062c7a2c/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfdadbce1bcc63a691f14fcd569cf32fc0bd32d6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29536, "bytes_utf8": 29536, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 623, "bytes_utf8": 623, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20370, "cached_input_tokens": 19840, "output_tokens": 607, "reasoning_output_tokens": 442}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..62f1b3affe325b1172d43cccab6efd2533ed81e4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_9b677f040a887697",
+    "api_calls": 0,
+    "input_tokens": 20370,
+    "cached_input_tokens": 19840,
+    "output_tokens": 607,
+    "total_tokens": 20977,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18807.39,
+    "sql_execution_elapsed_ms_total": 15.54,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9d3eb8232d50ba1f94227f84957bba69a7c9c89
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:45:16.806387+00:00",
+  "ended_at": "2026-05-19T15:45:35.613811+00:00",
+  "elapsed_ms": 18807.39,
+  "prompt_metrics": {
+    "chars": 29536,
+    "bytes_utf8": 29536,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1335,
+    "bytes_utf8": 1335,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 623,
+      "bytes_utf8": 623,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20370,
+      "cached_input_tokens": 19840,
+      "output_tokens": 607,
+      "reasoning_output_tokens": 442
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..909f871b78e6dbb3716468831308177a1dc497e7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_remove.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "word_freq_remove",
+    "top_k": 13,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4f404d58c5ce125a06040afe9623254e23ce0a04
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e9-af04-7302-aaf9-6e2598fcbed2"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 21005. Please try again in 630ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n    SELECT CAST(\\\"word_freq_remove\\\" AS REAL) AS \\\"word_freq_remove\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_remove\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n    FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_remove\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_remove\\\" DESC;\",\"notes\":\"Used the provided Quantile Tail Slice template with measure_col bound to \\\"word_freq_remove\\\" and num_tiles=10. Cast to REAL because the schema stores numeric features as TEXT, and numeric ordering is required for correct tail bucketing.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20370,"cached_input_tokens":19840,"output_tokens":607,"reasoning_output_tokens":442}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3dc0f5b8b923fd416430d77c5a3f9549d7c0b4ec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n    SELECT CAST(\"word_freq_remove\" AS REAL) AS \"word_freq_remove\",\n           NTILE(10) OVER (ORDER BY CAST(\"word_freq_remove\" AS REAL) DESC) AS \"tail_bucket\"\n    FROM \"n1\"\n)\nSELECT \"word_freq_remove\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_remove\" DESC;","notes":"Used the provided Quantile Tail Slice template with measure_col bound to \"word_freq_remove\" and num_tiles=10. Cast to REAL because the schema stores numeric features as TEXT, and numeric ordering is required for correct tail bucketing."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9b677f040a887697/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc93225412d76977ab8240c46c811c833819ce16
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"word_freq_free": "0", "support": 3360, "avg_response": 0.2298035714285714}, {"word_freq_free": "0.1", "support": 33, "avg_response": 0.13090909090909092}, {"word_freq_free": "0.32", "support": 31, "avg_response": 0.11032258064516129}, {"word_freq_free": "0.25", "support": 24, "avg_response": 0.19999999999999998}, {"word_freq_free": "0.23", "support": 23, "avg_response": 0.2252173913043478}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..f2d10f658d1c7841fdb654311b25a1cd0c4001b0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n1_a002566835012e27
+-- problem_id: v2p_n1_6b7b01ccd249abb0
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "word_freq_free",
+    COUNT(*) AS support,
+    AVG("word_freq_address") AS avg_response
+FROM "n1"
+GROUP BY "word_freq_free"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05227645d56daced57fbcaf7c836e9af6b07996a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n1_a002566835012e27\n-- problem_id: v2p_n1_6b7b01ccd249abb0\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"word_freq_free\",\n    COUNT(*) AS support,\n    AVG(\"word_freq_address\") AS avg_response\nFROM \"n1\"\nGROUP BY \"word_freq_free\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n1_a002566835012e27\\n-- problem_id: v2p_n1_6b7b01ccd249abb0\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"word_freq_free\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"word_freq_address\\\") AS avg_response\\nFROM \\\"n1\\\"\\nGROUP BY \\\"word_freq_free\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"word_freq_free\", \"support\", \"avg_response\"], \"rows\": [{\"word_freq_free\": \"0\", \"support\": 3360, \"avg_response\": 0.2298035714285714}, {\"word_freq_free\": \"0.1\", \"support\": 33, \"avg_response\": 0.13090909090909092}, {\"word_freq_free\": \"0.32\", \"support\": 31, \"avg_response\": 0.11032258064516129}, {\"word_freq_free\": \"0.25\", \"support\": 24, \"avg_response\": 0.19999999999999998}, {\"word_freq_free\": \"0.23\", \"support\": 23, \"avg_response\": 0.2252173913043478}, {\"word_freq_free\": \"0.38\", \"support\": 21, \"avg_response\": 0.13714285714285715}, {\"word_freq_free\": \"0.19\", \"support\": 19, \"avg_response\": 0.21526315789473682}, {\"word_freq_free\": \"0.14\", \"support\": 18, \"avg_response\": 0.16944444444444445}, {\"word_freq_free\": \"0.08\", \"support\": 17, \"avg_response\": 0.12823529411764706}, {\"word_freq_free\": \"0.48\", \"support\": 17, \"avg_response\": 0.12294117647058822}, {\"word_freq_free\": \"0.58\", \"support\": 17, \"avg_response\": 0.03411764705882353}, {\"word_freq_free\": \"0.26\", \"support\": 16, \"avg_response\": 0.21375}, {\"word_freq_free\": \"0.36\", \"support\": 16, \"avg_response\": 0.135}, {\"word_freq_free\": \"0.13\", \"support\": 16, \"avg_response\": 0.1225}, {\"word_freq_free\": \"0.47\", \"support\": 15, \"avg_response\": 0.31466666666666665}, {\"word_freq_free\": \"0.17\", \"support\": 15, \"avg_response\": 0.1366666666666667}, {\"word_freq_free\": \"0.3\", \"support\": 15, \"avg_response\": 0.02}, {\"word_freq_free\": \"0.09\", \"support\": 14, \"avg_response\": 0.075}, {\"word_freq_free\": \"0.8\", \"support\": 13, \"avg_response\": 0.3076923076923077}, {\"word_freq_free\": \"0.41\", \"support\": 13, \"avg_response\": 0.1576923076923077}, {\"word_freq_free\": \"0.11\", \"support\": 13, \"avg_response\": 0.09923076923076923}, {\"word_freq_free\": \"0.54\", \"support\": 13, \"avg_response\": 0.08}, {\"word_freq_free\": \"0.49\", \"support\": 12, \"avg_response\": 0.2575}, {\"word_freq_free\": \"0.66\", \"support\": 12, \"avg_response\": 0.25166666666666665}, {\"word_freq_free\": \"0.56\", \"support\": 12, \"avg_response\": 0.24833333333333338}, {\"word_freq_free\": \"0.44\", \"support\": 12, \"avg_response\": 0.18333333333333335}, {\"word_freq_free\": \"0.27\", \"support\": 12, \"avg_response\": 0.15083333333333335}, {\"word_freq_free\": \"0.31\", \"support\": 12, \"avg_response\": 0.11583333333333333}, {\"word_freq_free\": \"0.4\", \"support\": 12, \"avg_response\": 0.10916666666666668}, {\"word_freq_free\": \"0.29\", \"support\": 12, \"avg_response\": 0.0725}, {\"word_freq_free\": \"0.64\", \"support\": 12, \"avg_response\": 0.06666666666666667}, {\"word_freq_free\": \"0.68\", \"support\": 12, \"avg_response\": 0.065}, {\"word_freq_free\": \"0.98\", \"support\": 11, \"avg_response\": 0.46181818181818185}, {\"word_freq_free\": \"0.37\", \"support\": 11, \"avg_response\": 0.32090909090909087}, {\"word_freq_free\": \"0.45\", \"support\": 11, \"avg_response\": 0.30727272727272725}, {\"word_freq_free\": \"0.16\", \"support\": 11, \"avg_response\": 0.27636363636363637}, {\"word_freq_free\": \"0.55\", \"support\": 11, \"avg_response\": 0.2}, {\"word_freq_free\": \"0.24\", \"support\": 11, \"avg_response\": 0.1590909090909091}, {\"word_freq_free\": \"0.22\", \"support\": 11, \"avg_response\": 0.14181818181818182}, {\"word_freq_free\": \"0.6\", \"support\": 11, \"avg_response\": 0.018181818181818184}, {\"word_freq_free\": \"0.21\", \"support\": 10, \"avg_response\": 0.26}, {\"word_freq_free\": \"0.33\", \"support\": 10, \"avg_response\": 0.173}, {\"word_freq_free\": \"0.62\", \"support\": 10, \"avg_response\": 0.11400000000000002}, {\"word_freq_free\": \"0.5\", \"support\": 10, \"avg_response\": 0.083}, {\"word_freq_free\": \"0.87\", \"support\": 10, \"avg_response\": 0.05800000000000001}, {\"word_freq_free\": \"0.86\", \"support\": 9, \"avg_response\": 0.11555555555555556}, {\"word_freq_free\": \"1.05\", \"support\": 9, \"avg_response\": 0.09666666666666666}, {\"word_freq_free\": \"0.05\", \"support\": 9, \"avg_response\": 0.08888888888888888}, {\"word_freq_free\": \"0.43\", \"support\": 9, \"avg_response\": 0.07111111111111111}, {\"word_freq_free\": \"0.82\", \"support\": 8, \"avg_response\": 0.3825}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.24}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..fed7b93b69a2b0338a730eaafb65ee36e75c3029
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/run_manifest.json
@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.199420+00:00",
+  "ended_at": "2026-05-19T16:06:30.202449+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_a002566835012e27",
+    "problem_id": "v2p_n1_6b7b01ccd249abb0",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=word_freq_address, key_col=word_freq_free.",
+    "bindings": {
+      "key_col": "word_freq_free",
+      "measure_col": "word_freq_address",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=10",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 10,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_a002566835012e27.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a002566835012e27/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d055c2bd7b394d135b9b0a47ae770712b2a829f7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"word_freq_our": "0", "support": 2853, "avg_response": 0.08781282860147213}, {"word_freq_our": "0.36", "support": 28, "avg_response": 0.12285714285714286}, {"word_freq_our": "0.32", "support": 26, "avg_response": 0.15461538461538463}, {"word_freq_our": "0.19", "support": 24, "avg_response": 0.1625}, {"word_freq_our": "0.8", "support": 24, "avg_response": 0.08333333333333333}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..84eb8384cf9b9286d607376782977dcdd282a349
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n1_a2e65b1631df59d2
+-- problem_id: v2p_n1_703b7745ebd93b4c
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "word_freq_our",
+    COUNT(*) AS support,
+    AVG("word_freq_make") AS avg_response
+FROM "n1"
+GROUP BY "word_freq_our"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dcb1919e8861c5e632d44587ba5c7d03d8b0abd3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n1_a2e65b1631df59d2\n-- problem_id: v2p_n1_703b7745ebd93b4c\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"word_freq_our\",\n    COUNT(*) AS support,\n    AVG(\"word_freq_make\") AS avg_response\nFROM \"n1\"\nGROUP BY \"word_freq_our\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n1_a2e65b1631df59d2\\n-- problem_id: v2p_n1_703b7745ebd93b4c\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"word_freq_our\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"word_freq_make\\\") AS avg_response\\nFROM \\\"n1\\\"\\nGROUP BY \\\"word_freq_our\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"word_freq_our\", \"support\", \"avg_response\"], \"rows\": [{\"word_freq_our\": \"0\", \"support\": 2853, \"avg_response\": 0.08781282860147213}, {\"word_freq_our\": \"0.36\", \"support\": 28, \"avg_response\": 0.12285714285714286}, {\"word_freq_our\": \"0.32\", \"support\": 26, \"avg_response\": 0.15461538461538463}, {\"word_freq_our\": \"0.19\", \"support\": 24, \"avg_response\": 0.1625}, {\"word_freq_our\": \"0.8\", \"support\": 24, \"avg_response\": 0.08333333333333333}, {\"word_freq_our\": \"0.14\", \"support\": 22, \"avg_response\": 0.30818181818181817}, {\"word_freq_our\": \"0.26\", \"support\": 22, \"avg_response\": 0.11545454545454546}, {\"word_freq_our\": \"0.29\", \"support\": 22, \"avg_response\": 0.10636363636363635}, {\"word_freq_our\": \"0.53\", \"support\": 21, \"avg_response\": 0.20047619047619047}, {\"word_freq_our\": \"0.45\", \"support\": 21, \"avg_response\": 0.1742857142857143}, {\"word_freq_our\": \"0.23\", \"support\": 21, \"avg_response\": 0.14761904761904762}, {\"word_freq_our\": \"0.34\", \"support\": 21, \"avg_response\": 0.07952380952380952}, {\"word_freq_our\": \"0.68\", \"support\": 21, \"avg_response\": 0.053809523809523814}, {\"word_freq_our\": \"0.64\", \"support\": 21, \"avg_response\": 0.0}, {\"word_freq_our\": \"0.13\", \"support\": 20, \"avg_response\": 0.21350000000000002}, {\"word_freq_our\": \"0.4\", \"support\": 20, \"avg_response\": 0.07150000000000001}, {\"word_freq_our\": \"0.65\", \"support\": 20, \"avg_response\": 0.006500000000000001}, {\"word_freq_our\": \"0.25\", \"support\": 19, \"avg_response\": 0.2810526315789474}, {\"word_freq_our\": \"0.43\", \"support\": 19, \"avg_response\": 0.12157894736842105}, {\"word_freq_our\": \"0.27\", \"support\": 19, \"avg_response\": 0.11526315789473685}, {\"word_freq_our\": \"0.12\", \"support\": 19, \"avg_response\": 0.10210526315789474}, {\"word_freq_our\": \"0.09\", \"support\": 19, \"avg_response\": 0.08578947368421053}, {\"word_freq_our\": \"0.33\", \"support\": 19, \"avg_response\": 0.060000000000000005}, {\"word_freq_our\": \"0.38\", \"support\": 19, \"avg_response\": 0.05421052631578947}, {\"word_freq_our\": \"0.15\", \"support\": 18, \"avg_response\": 0.19277777777777777}, {\"word_freq_our\": \"0.44\", \"support\": 18, \"avg_response\": 0.17777777777777778}, {\"word_freq_our\": \"0.28\", \"support\": 18, \"avg_response\": 0.13611111111111113}, {\"word_freq_our\": \"0.22\", \"support\": 18, \"avg_response\": 0.12888888888888891}, {\"word_freq_our\": \"0.39\", \"support\": 18, \"avg_response\": 0.12277777777777778}, {\"word_freq_our\": \"0.42\", \"support\": 18, \"avg_response\": 0.06833333333333333}, {\"word_freq_our\": \"0.17\", \"support\": 17, \"avg_response\": 0.14529411764705882}, {\"word_freq_our\": \"0.1\", \"support\": 17, \"avg_response\": 0.0611764705882353}, {\"word_freq_our\": \"0.3\", \"support\": 17, \"avg_response\": 0.039411764705882354}, {\"word_freq_our\": \"0.47\", \"support\": 16, \"avg_response\": 0.200625}, {\"word_freq_our\": \"0.54\", \"support\": 16, \"avg_response\": 0.176875}, {\"word_freq_our\": \"0.52\", \"support\": 16, \"avg_response\": 0.17625000000000002}, {\"word_freq_our\": \"0.6\", \"support\": 16, \"avg_response\": 0.051250000000000004}, {\"word_freq_our\": \"0.31\", \"support\": 16, \"avg_response\": 0.018750000000000003}, {\"word_freq_our\": \"0.05\", \"support\": 15, \"avg_response\": 0.31}, {\"word_freq_our\": \"0.08\", \"support\": 15, \"avg_response\": 0.2733333333333334}, {\"word_freq_our\": \"0.87\", \"support\": 15, \"avg_response\": 0.1753333333333333}, {\"word_freq_our\": \"0.16\", \"support\": 15, \"avg_response\": 0.15666666666666668}, {\"word_freq_our\": \"0.2\", \"support\": 15, \"avg_response\": 0.11933333333333333}, {\"word_freq_our\": \"0.85\", \"support\": 14, \"avg_response\": 0.14785714285714285}, {\"word_freq_our\": \"0.24\", \"support\": 14, \"avg_response\": 0.12928571428571428}, {\"word_freq_our\": \"0.35\", \"support\": 14, \"avg_response\": 0.06714285714285714}, {\"word_freq_our\": \"0.51\", \"support\": 14, \"avg_response\": 0.034999999999999996}, {\"word_freq_our\": \"1.11\", \"support\": 14, \"avg_response\": 0.019285714285714288}, {\"word_freq_our\": \"0.76\", \"support\": 13, \"avg_response\": 0.019230769230769232}, {\"word_freq_our\": \"0.48\", \"support\": 12, \"avg_response\": 0.255}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.65}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..5571ef52a6fd5c880d735c81454016af38e30866
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/run_manifest.json
@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.175045+00:00",
+  "ended_at": "2026-05-19T16:06:30.178652+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_a2e65b1631df59d2",
+    "problem_id": "v2p_n1_703b7745ebd93b4c",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=word_freq_make, key_col=word_freq_our.",
+    "bindings": {
+      "key_col": "word_freq_our",
+      "measure_col": "word_freq_make",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_a2e65b1631df59d2.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a2e65b1631df59d2/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c1ae7188ac8cddeeeb6c5bce9aaf8c4027015832
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.
+Result preview: [{"class": "1", "total_measure": 298.55}, {"class": "0", "total_measure": 2.47}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..245f3b5abc76873d8f8a95bb7148d0f48fa71488
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n1_a4cf422837fa3733
+-- problem_id: v2p_n1_1c80dcc83027ee55
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_3d" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+ORDER BY total_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be91fc6ea50a5411f75fb280260425f3dfd9b349
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_3d\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 298.55}, {\"class\": \"0\", \"total_measure\": 2.47}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.18}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed5097cc4b1ee48f904a641481d14152db6cb19d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:29:14.738386+00:00",
+  "ended_at": "2026-05-19T15:29:23.657343+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_a4cf422837fa3733",
+    "problem_id": "v2p_n1_1c80dcc83027ee55",
+    "dataset_id": "n1",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_3d",
+      "top_k": 13,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/2",
+      "binding_index=3"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_a4cf422837fa3733.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a4cf422837fa3733",
+    "api_calls": 0,
+    "input_tokens": 20319,
+    "cached_input_tokens": 12288,
+    "output_tokens": 332,
+    "total_tokens": 20651,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8910.25,
+    "sql_execution_elapsed_ms_total": 2.18,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e65b0bf6eef9b62baeff80cda6db6fdfeb83fae
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:29:23.653795+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8910.25, "started_at": "2026-05-19T15:29:14.742684+00:00", "ended_at": "2026-05-19T15:29:23.652970+00:00", "prompt_metrics": {"chars": 29351, "bytes_utf8": 29351, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 366, "bytes_utf8": 366, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20319, "cached_input_tokens": 12288, "output_tokens": 332, "reasoning_output_tokens": 230}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the planned Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_3d\\\". The measure is cast to REAL because the schema stores values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5082514783b36fd889fd33b6a8dc2a45fbe6b67a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_a4cf422837fa3733",
+  "api_calls": 0,
+  "input_tokens": 20319,
+  "cached_input_tokens": 12288,
+  "output_tokens": 332,
+  "total_tokens": 20651,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 8910.25,
+  "sql_execution_elapsed_ms_total": 2.18,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d276bbf290fb50a72e41d5ff4bd2141336a57aff
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.
+Result preview: [{"class": "0", "condition_rate": 1.0}, {"class": "1", "condition_rate": 0.0}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..10520ec4e2c94a33eafc18370e42e5a5900233be
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_n1_a6e7250181274940
+-- problem_id: v2p_n1_870706dd3c32b883
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class",
+       AVG(CASE WHEN "class" = '0' THEN 1 ELSE 0 END) AS condition_rate
+FROM "n1"
+GROUP BY "class"
+ORDER BY condition_rate DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f87334d8ef6407073bd4992955caef85f9359453
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"class\", \"condition_rate\"], \"rows\": [{\"class\": \"0\", \"condition_rate\": 1.0}, {\"class\": \"1\", \"condition_rate\": 0.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.0}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e552ba765d916c73f066bbf9cc60d3be37a207b5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/run_manifest.json
@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:59:13.525816+00:00",
+  "ended_at": "2026-05-19T15:59:24.529064+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_a6e7250181274940",
+    "problem_id": "v2p_n1_870706dd3c32b883",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "condition_col": "class",
+      "condition_value": "0",
+      "positive_value": "0",
+      "negative_value": "1",
+      "top_k": 10,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/2",
+      "binding_index=100"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_a6e7250181274940.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a6e7250181274940",
+    "api_calls": 0,
+    "input_tokens": 20373,
+    "cached_input_tokens": 19840,
+    "output_tokens": 290,
+    "total_tokens": 20663,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10995.98,
+    "sql_execution_elapsed_ms_total": 2.0,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd3469257fe0296ad5573ea96d2366defa11ea9d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:59:24.525626+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10995.98, "started_at": "2026-05-19T15:59:13.528837+00:00", "ended_at": "2026-05-19T15:59:24.524847+00:00", "prompt_metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}, "response_metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 19840, "output_tokens": 290, "reasoning_output_tokens": 181}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with \\\"class\\\" as both the grouping column and condition column; compared against text value '0' because the schema stores \\\"class\\\" as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..0086d0b226aa42ae85e994a0f8f67bb4028e4ded
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_a6e7250181274940",
+  "api_calls": 0,
+  "input_tokens": 20373,
+  "cached_input_tokens": 19840,
+  "output_tokens": 290,
+  "total_tokens": 20663,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10995.98,
+  "sql_execution_elapsed_ms_total": 2.0,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e4ed2de30ea96aa4272c9b82db8fd40fd7dabd43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:09.204533+00:00",
+  "ended_at": "2026-05-19T15:56:12.506838+00:00",
+  "elapsed_ms": 3302.27,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f16e60e822ac3447459ba473aa7219b5f2766ed8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:13.510226+00:00",
+  "ended_at": "2026-05-19T15:56:16.442854+00:00",
+  "elapsed_ms": 2932.58,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f65bcb37ce1050e88411bc5027bb4d728cc2046a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_415.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_415",
+    "top_k": 10,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f65bcb37ce1050e88411bc5027bb4d728cc2046a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_415.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_415",
+    "top_k": 10,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a182c0304a0ac2ae80c026430f6ea7de11b5be0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-a366-73d0-9668-c5f67f2b335a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2da9dffae73d4c4d050283128edeff82be73565
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-a366-73d0-9668-c5f67f2b335a"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b6b01756b6c326353114dc2df1493c58d49619e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-b45b-7561-b180-dec9511230f2"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..58495247b5b53d38f777eee4d55aece5b86cae73
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-b45b-7561-b180-dec9511230f2"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7457a4b59f797bf/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..179893e199bb3e50b7fbe77b6787815f91674648
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29772, "bytes_utf8": 29772, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 668, "bytes_utf8": 668, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 19840, "output_tokens": 433, "reasoning_output_tokens": 251}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1fdd7e1f0a5af03e9f24eca39359ca86625e0cf8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a7833920334a8219",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 19840,
+    "output_tokens": 433,
+    "total_tokens": 20870,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9263.94,
+    "sql_execution_elapsed_ms_total": 4.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2169b08b3d446a53a6b8d38b5f1bffd3811cb20e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:35:11.278875+00:00",
+  "ended_at": "2026-05-19T15:35:20.542841+00:00",
+  "elapsed_ms": 9263.94,
+  "prompt_metrics": {
+    "chars": 29772,
+    "bytes_utf8": 29772,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1048,
+    "bytes_utf8": 1048,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 668,
+      "bytes_utf8": 668,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 19840,
+      "output_tokens": 433,
+      "reasoning_output_tokens": 251
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..33e36e3e3a042bf4481d6af0cfcdfc2958156930
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_george.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_george",
+    "item_col": "word_freq_650",
+    "top_k": 16,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9345fb3d750dee6d215fce57fd7cfd6b2344bfe
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e0-71c1-7f40-8ba4-43bd6b7c3840"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_650\\\",\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the required Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_650\\\", and measure_col=\\\"word_freq_george\\\". Cast the TEXT-typed measure column to REAL so SUM and percentage calculations behave numerically in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":19840,"output_tokens":433,"reasoning_output_tokens":251}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d5a8deee05781cbd3cc50bcd8992cb5f1f123612
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_650\",\n       SUM(CAST(\"word_freq_george\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_george\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_george\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_650\"\nORDER BY share_within_group DESC;","notes":"Applied the required Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_650\", and measure_col=\"word_freq_george\". Cast the TEXT-typed measure column to REAL so SUM and percentage calculations behave numerically in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc3b8d6445dc2fa0849b541a5410cdc878728667
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_george.
+Result preview: [{"class": "1", "word_freq_650": "0", "total_measure": 2.81, "share_within_group": 100.0}, {"class": "0", "word_freq_650": "0", "total_measure": 3156.36, "share_within_group": 89.47714567576455}, {"class": "0", "word_freq_650": "4.76", "total_measure": 28.56, "share_within_group": 0.8096247831362188}, {"class": "0", "word_freq_650": "4.34", "total_measure": 13.02, "share_within_group": 0.36909365113562914}, {"class": "0", "word_freq_650": "4.16", "total_measure": 12.48, "share_within_group": 0.35378561952170906}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..77cd484ed34b2b44ed0ddef6bb100c8c3ae96d43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/generated_sql.sql
@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_a7833920334a8219
+-- problem_id: v2p_n1_12f30bb12346fcd8
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", "word_freq_650",
+       SUM(CAST("word_freq_george" AS REAL)) AS total_measure,
+       SUM(CAST("word_freq_george" AS REAL)) * 100.0 / SUM(SUM(CAST("word_freq_george" AS REAL))) OVER (PARTITION BY "class") AS share_within_group
+FROM "n1"
+GROUP BY "class", "word_freq_650"
+ORDER BY share_within_group DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b07abe0ce44dcdbaf15a8fd23be7ea9ad3d842c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_650\",\n       SUM(CAST(\"word_freq_george\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_george\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_george\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_650\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_650\\\",\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"class\", \"word_freq_650\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_650\": \"0\", \"total_measure\": 2.81, \"share_within_group\": 100.0}, {\"class\": \"0\", \"word_freq_650\": \"0\", \"total_measure\": 3156.36, \"share_within_group\": 89.47714567576455}, {\"class\": \"0\", \"word_freq_650\": \"4.76\", \"total_measure\": 28.56, \"share_within_group\": 0.8096247831362188}, {\"class\": \"0\", \"word_freq_650\": \"4.34\", \"total_measure\": 13.02, \"share_within_group\": 0.36909365113562914}, {\"class\": \"0\", \"word_freq_650\": \"4.16\", \"total_measure\": 12.48, \"share_within_group\": 0.35378561952170906}, {\"class\": \"0\", \"word_freq_650\": \"2.04\", \"total_measure\": 8.67, \"share_within_group\": 0.245778952023495}, {\"class\": \"0\", \"word_freq_650\": \"0.5\", \"total_measure\": 8.4, \"share_within_group\": 0.23812493621653494}, {\"class\": \"0\", \"word_freq_650\": \"2.77\", \"total_measure\": 6.92, \"share_within_group\": 0.19616959031171688}, {\"class\": \"0\", \"word_freq_650\": \"2.63\", \"total_measure\": 6.13, \"share_within_group\": 0.1737745070246856}, {\"class\": \"0\", \"word_freq_650\": \"1.01\", \"total_measure\": 6.06, \"share_within_group\": 0.1717901325562145}, {\"class\": \"0\", \"word_freq_650\": \"0.86\", \"total_measure\": 6.05, \"share_within_group\": 0.17150665048929006}, {\"class\": \"0\", \"word_freq_650\": \"4\", \"total_measure\": 6.0, \"share_within_group\": 0.1700892401546678}, {\"class\": \"0\", \"word_freq_650\": \"5.88\", \"total_measure\": 5.88, \"share_within_group\": 0.16668745535157445}, {\"class\": \"0\", \"word_freq_650\": \"0.63\", \"total_measure\": 5.7299999999999995, \"share_within_group\": 0.16243522434770777}, {\"class\": \"0\", \"word_freq_650\": \"0.76\", \"total_measure\": 5.34, \"share_within_group\": 0.15137942373765437}, {\"class\": \"0\", \"word_freq_650\": \"0.66\", \"total_measure\": 5.32, \"share_within_group\": 0.15081245960380546}, {\"class\": \"0\", \"word_freq_650\": \"1.31\", \"total_measure\": 5.26, \"share_within_group\": 0.1491115672022588}, {\"class\": \"0\", \"word_freq_650\": \"0.68\", \"total_measure\": 5.130000000000001, \"share_within_group\": 0.14542630033224102}, {\"class\": \"0\", \"word_freq_650\": \"0.58\", \"total_measure\": 4.9799999999999995, \"share_within_group\": 0.14117406932837429}, {\"class\": \"0\", \"word_freq_650\": \"0.8\", \"total_measure\": 4.8100000000000005, \"share_within_group\": 0.13635487419065873}, {\"class\": \"0\", \"word_freq_650\": \"1.28\", \"total_measure\": 4.69, \"share_within_group\": 0.13295308938756537}, {\"class\": \"0\", \"word_freq_650\": \"3.12\", \"total_measure\": 4.68, \"share_within_group\": 0.1326696073206409}, {\"class\": \"0\", \"word_freq_650\": \"2.32\", \"total_measure\": 4.64, \"share_within_group\": 0.13153567905294308}, {\"class\": \"0\", \"word_freq_650\": \"4.54\", \"total_measure\": 4.54, \"share_within_group\": 0.12870085838369866}, {\"class\": \"0\", \"word_freq_650\": \"0.64\", \"total_measure\": 4.34, \"share_within_group\": 0.12303121704520972}, {\"class\": \"0\", \"word_freq_650\": \"1.08\", \"total_measure\": 4.34, \"share_within_group\": 0.12303121704520972}, {\"class\": \"0\", \"word_freq_650\": \"1.44\", \"total_measure\": 4.34, \"share_within_group\": 0.12303121704520972}, {\"class\": \"0\", \"word_freq_650\": \"0.61\", \"total_measure\": 4.28, \"share_within_group\": 0.12133032464366304}, {\"class\": \"0\", \"word_freq_650\": \"1.42\", \"total_measure\": 4.28, \"share_within_group\": 0.12133032464366304}, {\"class\": \"0\", \"word_freq_650\": \"0.39\", \"total_measure\": 4.13, \"share_within_group\": 0.11707809363979635}, {\"class\": \"0\", \"word_freq_650\": \"0.77\", \"total_measure\": 3.87, \"share_within_group\": 0.10970755989976075}, {\"class\": \"0\", \"word_freq_650\": \"0.29\", \"total_measure\": 3.84, \"share_within_group\": 0.1088571136989874}, {\"class\": \"0\", \"word_freq_650\": \"3.84\", \"total_measure\": 3.84, \"share_within_group\": 0.1088571136989874}, {\"class\": \"0\", \"word_freq_650\": \"0.37\", \"total_measure\": 3.7300000000000004, \"share_within_group\": 0.10573881096281851}, {\"class\": \"0\", \"word_freq_650\": \"0.93\", \"total_measure\": 3.72, \"share_within_group\": 0.10545532889589404}, {\"class\": \"0\", \"word_freq_650\": \"2.46\", \"total_measure\": 3.7, \"share_within_group\": 0.10488836476204516}, {\"class\": \"0\", \"word_freq_650\": \"0.51\", \"total_measure\": 3.59, \"share_within_group\": 0.10177006202587624}, {\"class\": \"0\", \"word_freq_650\": \"3.57\", \"total_measure\": 3.57, \"share_within_group\": 0.10120309789202735}, {\"class\": \"0\", \"word_freq_650\": \"0.88\", \"total_measure\": 3.52, \"share_within_group\": 0.09978568755740512}, {\"class\": \"0\", \"word_freq_650\": \"1.72\", \"total_measure\": 3.44, \"share_within_group\": 0.09751783102200955}, {\"class\": \"0\", \"word_freq_650\": \"0.85\", \"total_measure\": 3.41, \"share_within_group\": 0.09666738482123621}, {\"class\": \"0\", \"word_freq_650\": \"0.55\", \"total_measure\": 3.3200000000000003, \"share_within_group\": 0.0941160462189162}, {\"class\": \"0\", \"word_freq_650\": \"0.65\", \"total_measure\": 3.2800000000000002, \"share_within_group\": 0.09298211795121841}, {\"class\": \"0\", \"word_freq_650\": \"1.29\", \"total_measure\": 3.22, \"share_within_group\": 0.09128122554967173}, {\"class\": \"0\", \"word_freq_650\": \"2.35\", \"total_measure\": 3.05, \"share_within_group\": 0.08646203041195615}, {\"class\": \"0\", \"word_freq_650\": \"3.03\", \"total_measure\": 3.03, \"share_within_group\": 0.08589506627810725}, {\"class\": \"0\", \"word_freq_650\": \"0.33\", \"total_measure\": 3.02, \"share_within_group\": 0.0856115842111828}, {\"class\": \"0\", \"word_freq_650\": \"0.91\", \"total_measure\": 2.75, \"share_within_group\": 0.07795756840422274}, {\"class\": \"0\", \"word_freq_650\": \"0.87\", \"total_measure\": 2.63, \"share_within_group\": 0.0745557836011294}, {\"class\": \"0\", \"word_freq_650\": \"2.56\", \"total_measure\": 2.56, \"share_within_group\": 0.07257140913265826}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 4.25}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..6035fa580f2bbb7fa1c81a673607758fa8244118
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:35:11.276128+00:00",
+  "ended_at": "2026-05-19T15:35:20.549733+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_a7833920334a8219",
+    "problem_id": "v2p_n1_12f30bb12346fcd8",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_george.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_george",
+      "item_col": "word_freq_650",
+      "top_k": 16,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=2/2",
+      "binding_index=26"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_a7833920334a8219.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a7833920334a8219",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 19840,
+    "output_tokens": 433,
+    "total_tokens": 20870,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9263.94,
+    "sql_execution_elapsed_ms_total": 4.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c3d370eba18aac82a4d220dbeb7e85fa1aedead
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:35:20.543923+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9263.94, "started_at": "2026-05-19T15:35:11.278875+00:00", "ended_at": "2026-05-19T15:35:20.542841+00:00", "prompt_metrics": {"chars": 29772, "bytes_utf8": 29772, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 668, "bytes_utf8": 668, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 19840, "output_tokens": 433, "reasoning_output_tokens": 251}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_650\\\",\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the required Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_650\\\", and measure_col=\\\"word_freq_george\\\". Cast the TEXT-typed measure column to REAL so SUM and percentage calculations behave numerically in SQLite.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..67865ce8558309a4a32a855009c6c704ca3a11a9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_a7833920334a8219",
+  "api_calls": 0,
+  "input_tokens": 20437,
+  "cached_input_tokens": 19840,
+  "output_tokens": 433,
+  "total_tokens": 20870,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 9263.94,
+  "sql_execution_elapsed_ms_total": 4.25,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7833920334a8219/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4cf8697d00f3ba18e9bd6d8aa32939bcbe60441d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29357, "bytes_utf8": 29357, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 360, "bytes_utf8": 360, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 364, "reasoning_output_tokens": 266}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a07424b9f9336778e7a3d7f3877ca122f7b0d87
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a7b2792591710337",
+    "api_calls": 0,
+    "input_tokens": 20315,
+    "cached_input_tokens": 12032,
+    "output_tokens": 364,
+    "total_tokens": 20679,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8997.04,
+    "sql_execution_elapsed_ms_total": 3.56,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..82a34860f85f666038484169431833bf3ecc5ae8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:28.344238+00:00",
+  "ended_at": "2026-05-19T15:28:37.341304+00:00",
+  "elapsed_ms": 8997.04,
+  "prompt_metrics": {
+    "chars": 29357,
+    "bytes_utf8": 29357,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 722,
+    "bytes_utf8": 722,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 360,
+      "bytes_utf8": 360,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20315,
+      "cached_input_tokens": 12032,
+      "output_tokens": 364,
+      "reasoning_output_tokens": 266
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ce3e378879b9e1c5195dda9478379648c555636b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_make.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_make",
+    "top_k": 15,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..650a3488fdd0fa43dacb4ff12a1954f9465c96dc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-4bbb-70f2-8808-12b1b5b2ed8d"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_make\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the provided grouped numeric sum template with \\\"class\\\" as the group column and cast \\\"word_freq_make\\\" from TEXT to REAL so SQLite sums it numerically.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":364,"reasoning_output_tokens":266}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d4dcd73d56ce575066d96444f0545dd6d5f9aea2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_make\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Applied the provided grouped numeric sum template with \"class\" as the group column and cast \"word_freq_make\" from TEXT to REAL so SQLite sums it numerically."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a7b2792591710337/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ef689d893ff2ea1a2eef679e498650e6660f22d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1709, "bytes_utf8": 1709, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 1105, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e39967bf46f68af9d381657202f7474c05457168
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_a92b8abbd0b1e255",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 19840,
+    "output_tokens": 1105,
+    "total_tokens": 21460,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 17375.55,
+    "sql_execution_elapsed_ms_total": 30.47,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8a35ea930d14f9db24fa07580867b670a893e95
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:54:47.495074+00:00",
+  "ended_at": "2026-05-19T15:55:04.870663+00:00",
+  "elapsed_ms": 17375.55,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2312,
+    "bytes_utf8": 2312,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1709,
+      "bytes_utf8": 1709,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 19840,
+      "output_tokens": 1105,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b72fbb0c4eebb1551619bdf5aa2069b8b0f13be0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_857.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_857",
+    "top_k": 13,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..29d85dd01c9d801ee3a560858861fe7277af03b3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f2-6444-7c40-bba8-5ac8974e66d6"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_857\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_857\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"class\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_857\\\" IS NOT NULL\\n),\\n\\\"bounds\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    \\\"measure\\\",\\n    \\\"rn\\\",\\n    \\\"cnt\\\",\\n    ((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS \\\"pos\\\",\\n    CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN ((\\\"cnt\\\" - 1) * 0.95 + 1.0) = CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER)\\n        THEN CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER)\\n      ELSE CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER) + 1\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n),\\n\\\"percentiles\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\"measure\\\" END) AS \\\"lower_val\\\",\\n    MAX(CASE WHEN \\\"rn\\\" = \\\"upper_rn\\\" THEN \\\"measure\\\" END) AS \\\"upper_val\\\",\\n    MAX(\\\"pos\\\") AS \\\"pos\\\",\\n    MAX(\\\"lower_rn\\\") AS \\\"lower_rn\\\"\\n  FROM \\\"bounds\\\"\\n  GROUP BY \\\"class\\\"\\n)\\nSELECT\\n  \\\"class\\\",\\n  CASE\\n    WHEN \\\"pos\\\" = \\\"lower_rn\\\" THEN \\\"lower_val\\\"\\n    ELSE \\\"lower_val\\\" + ((\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\"))\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"percentiles\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Implements the grouped 95th percentile for \\\"word_freq_857\\\" by \\\"class\\\" in SQLite by emulating PERCENTILE_CONT(0.95) with window functions and linear interpolation, casting the TEXT-stored measure to REAL.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":1105,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ac3d6f3c76e7343fcf33c7fa98342c22384197e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_857\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_857\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"class\"\n    ) AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_857\" IS NOT NULL\n),\n\"bounds\" AS (\n  SELECT\n    \"class\",\n    \"measure\",\n    \"rn\",\n    \"cnt\",\n    ((\"cnt\" - 1) * 0.95 + 1.0) AS \"pos\",\n    CAST(((\"cnt\" - 1) * 0.95 + 1.0) AS INTEGER) AS \"lower_rn\",\n    CASE\n      WHEN ((\"cnt\" - 1) * 0.95 + 1.0) = CAST(((\"cnt\" - 1) * 0.95 + 1.0) AS INTEGER)\n        THEN CAST(((\"cnt\" - 1) * 0.95 + 1.0) AS INTEGER)\n      ELSE CAST(((\"cnt\" - 1) * 0.95 + 1.0) AS INTEGER) + 1\n    END AS \"upper_rn\"\n  FROM \"ranked\"\n),\n\"percentiles\" AS (\n  SELECT\n    \"class\",\n    MAX(CASE WHEN \"rn\" = \"lower_rn\" THEN \"measure\" END) AS \"lower_val\",\n    MAX(CASE WHEN \"rn\" = \"upper_rn\" THEN \"measure\" END) AS \"upper_val\",\n    MAX(\"pos\") AS \"pos\",\n    MAX(\"lower_rn\") AS \"lower_rn\"\n  FROM \"bounds\"\n  GROUP BY \"class\"\n)\nSELECT\n  \"class\",\n  CASE\n    WHEN \"pos\" = \"lower_rn\" THEN \"lower_val\"\n    ELSE \"lower_val\" + ((\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\"))\n  END AS \"percentile_measure\"\nFROM \"percentiles\"\nORDER BY \"percentile_measure\" DESC;","notes":"Implements the grouped 95th percentile for \"word_freq_857\" by \"class\" in SQLite by emulating PERCENTILE_CONT(0.95) with window functions and linear interpolation, casting the TEXT-stored measure to REAL."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb2774cf400650e9d55260dd6bbf7d352ec7b8de
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a92b8abbd0b1e255/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:55:04.872284+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17375.55, "started_at": "2026-05-19T15:54:47.495074+00:00", "ended_at": "2026-05-19T15:55:04.870663+00:00", "prompt_metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 1709, "bytes_utf8": 1709, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 1105, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_857\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_857\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"class\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_857\\\" IS NOT NULL\\n),\\n\\\"bounds\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    \\\"measure\\\",\\n    \\\"rn\\\",\\n    \\\"cnt\\\",\\n    ((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS \\\"pos\\\",\\n    CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN ((\\\"cnt\\\" - 1) * 0.95 + 1.0) = CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER)\\n        THEN CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER)\\n      ELSE CAST(((\\\"cnt\\\" - 1) * 0.95 + 1.0) AS INTEGER) + 1\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n),\\n\\\"percentiles\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    MAX(CASE WHEN \\\"rn\\\" = \\\"lower_rn\\\" THEN \\\""}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6499bc20eb4e2bdc8be09ebb09e1ceef482703e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29914, "bytes_utf8": 29914, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 641, "bytes_utf8": 641, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 394, "reasoning_output_tokens": 225}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..987634ab9a8d01c340f03a7a398f5b167c06cdb9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ab0dd1ecf1ba7371",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 12032,
+    "output_tokens": 394,
+    "total_tokens": 20845,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11149.23,
+    "sql_execution_elapsed_ms_total": 3.4,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab046ef67a09746ccb740c192001f3dceb6da096
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:48:14.607295+00:00",
+  "ended_at": "2026-05-19T15:48:25.756549+00:00",
+  "elapsed_ms": 11149.23,
+  "prompt_metrics": {
+    "chars": 29914,
+    "bytes_utf8": 29914,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1012,
+    "bytes_utf8": 1012,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 641,
+      "bytes_utf8": 641,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20451,
+      "cached_input_tokens": 12032,
+      "output_tokens": 394,
+      "reasoning_output_tokens": 225
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b07edc99bb292981fe70772b3c630ae95ea96239
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_credit.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_credit",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d2ac794bc5f85070aebce31cffcea9f00388844
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ec-6585-76b0-bd60-fb8ccaa287e6"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_credit\\\". Casted \\\"word_freq_credit\\\" to REAL because the schema stores it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":12032,"output_tokens":394,"reasoning_output_tokens":225}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ced0c59cde807c69ee14253dd99e511f17583315
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold.\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_credit\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_credit\". Casted \"word_freq_credit\" to REAL because the schema stores it as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1fb84aa8f466655f6a795e7a21f3db8c376c0002
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_credit.
+Result preview: [{"class": "1", "group_value": 372.61}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..8bcf09b305fa4542bb229760b52f9219e0cd195c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_ab0dd1ecf1ba7371
+-- problem_id: v2p_n1_85346da9f3f23c5f
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_credit" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.1
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a921f56a71201e5e22c0bd993558b4af6c45894
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold.\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_credit\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 372.61}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.4}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1b8093ed37665ad56cab2e6ce5dc152ba9e0babd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:48:14.605194+00:00",
+  "ended_at": "2026-05-19T15:48:25.762978+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_ab0dd1ecf1ba7371",
+    "problem_id": "v2p_n1_85346da9f3f23c5f",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_credit.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_credit",
+      "top_k": 11,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/2",
+      "binding_index=76"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_ab0dd1ecf1ba7371.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ab0dd1ecf1ba7371",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 12032,
+    "output_tokens": 394,
+    "total_tokens": 20845,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11149.23,
+    "sql_execution_elapsed_ms_total": 3.4,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31df791070b99ec16a003c5c37e85a8fb61f36dc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:48:25.757649+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11149.23, "started_at": "2026-05-19T15:48:14.607295+00:00", "ended_at": "2026-05-19T15:48:25.756549+00:00", "prompt_metrics": {"chars": 29914, "bytes_utf8": 29914, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 641, "bytes_utf8": 641, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 394, "reasoning_output_tokens": 225}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_credit\\\". Casted \\\"word_freq_credit\\\" to REAL because the schema stores it as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..df917318a22cb3a5479bed76d9f510de7c808371
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_ab0dd1ecf1ba7371",
+  "api_calls": 0,
+  "input_tokens": 20451,
+  "cached_input_tokens": 12032,
+  "output_tokens": 394,
+  "total_tokens": 20845,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11149.23,
+  "sql_execution_elapsed_ms_total": 3.4,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ab0dd1ecf1ba7371/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65681dae76339ab2fe36b631626d8038c0143f7d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 792, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 1823, "bytes_utf8": 1823, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 12032, "output_tokens": 2313, "reasoning_output_tokens": 1686}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..bfdcfa4d922109ff495cc2f14410a345ff39da5f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ac1ae7d00da00881",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 12032,
+    "output_tokens": 2313,
+    "total_tokens": 22668,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 34159.2,
+    "sql_execution_elapsed_ms_total": 38.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..684aa8da36d0f9e4d9cc67ef6314e0473310a849
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:55:04.911087+00:00",
+  "ended_at": "2026-05-19T15:55:08.084648+00:00",
+  "elapsed_ms": 3173.52,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0eb7313ce51fac19227c6864f7b84363a3479a49
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:55:09.088413+00:00",
+  "ended_at": "2026-05-19T15:55:40.074143+00:00",
+  "elapsed_ms": 30985.68,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2485,
+    "bytes_utf8": 2485,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1823,
+      "bytes_utf8": 1823,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 12032,
+      "output_tokens": 2313,
+      "reasoning_output_tokens": 1686
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..45164d72e399486e89a85826fcc160e2516efe83
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_857.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_857",
+    "top_k": 18,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..45164d72e399486e89a85826fcc160e2516efe83
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_857.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_857",
+    "top_k": 18,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2656b7626baa3787bbfa6db081262bccb72cd25a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f2-a844-7142-97ea-857462335df8"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b813a74a00a36865027ae6d86988a86f9f218864
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f2-a844-7142-97ea-857462335df8"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..801b5241a1b001de32a7e669143273480e046c67
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f2-b8ac-7921-bd9e-3d18f589db25"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n  SELECT\\n    \\\"class\\\" AS \\\"class\\\",\\n    CAST(\\\"word_freq_857\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_857\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"class\\\") AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"word_freq_857\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"class\\\",\\n    \\\"cnt\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) = CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER)\\n        THEN CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER)\\n      ELSE CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INTEGER) + 1\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ordered\\\"\\n)\\nSELECT\\n  \\\"o\\\".\\\"class\\\",\\n  CASE\\n    WHEN MAX(\\\"p\\\".\\\"lower_rn\\\") = MAX(\\\"p\\\".\\\"upper_rn\\\") THEN\\n      MAX(CASE WHEN \\\"o\\\".\\\"rn\\\" = \\\"p\\\".\\\"lower_rn\\\" THEN \\\"o\\\".\\\"measure\\\" END)\\n    ELSE\\n      MAX(CASE WHEN \\\"o\\\".\\\"rn\\\" = \\\"p\\\".\\\"lower_rn\\\" THEN \\\"o\\\".\\\"measure\\\" END) +\\n      (MAX(\\\"p\\\".\\\"pos\\\") - MAX(\\\"p\\\".\\\"lower_rn\\\")) * (\\n        MAX(CASE WHEN \\\"o\\\".\\\"rn\\\" = \\\"p\\\".\\\"upper_rn\\\" THEN \\\"o\\\".\\\"measure\\\" END) -\\n        MAX(CASE WHEN \\\"o\\\".\\\"rn\\\" = \\\"p\\\".\\\"lower_rn\\\" THEN \\\"o\\\".\\\"measure\\\" END)\\n      )\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"ordered\\\" AS \\\"o\\\"\\nJOIN \\\"positions\\\" AS \\\"p\\\"\\n  ON \\\"o\\\".\\\"class\\\" = \\\"p\\\".\\\"class\\\"\\nGROUP BY \\\"o\\\".\\\"class\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Emulates the template's 90th-percentile grouped point for \\\"class\\\" over CAST(\\\"word_freq_857\\\" AS REAL), using SQLite window functions and linear interpolation because SQLite does not support PERCENTILE_CONT and the source columns are stored as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":12032,"output_tokens":2313,"reasoning_output_tokens":1686}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..69bca28a3c85582569e60d07926b245b04bd4d1c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n  SELECT\n    \"class\" AS \"class\",\n    CAST(\"word_freq_857\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_857\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"class\") AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"word_freq_857\" IS NOT NULL\n),\n\"positions\" AS (\n  SELECT DISTINCT\n    \"class\",\n    \"cnt\",\n    (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n    CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER) AS \"lower_rn\",\n    CASE\n      WHEN (1.0 + 0.9 * (\"cnt\" - 1)) = CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER)\n        THEN CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER)\n      ELSE CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INTEGER) + 1\n    END AS \"upper_rn\"\n  FROM \"ordered\"\n)\nSELECT\n  \"o\".\"class\",\n  CASE\n    WHEN MAX(\"p\".\"lower_rn\") = MAX(\"p\".\"upper_rn\") THEN\n      MAX(CASE WHEN \"o\".\"rn\" = \"p\".\"lower_rn\" THEN \"o\".\"measure\" END)\n    ELSE\n      MAX(CASE WHEN \"o\".\"rn\" = \"p\".\"lower_rn\" THEN \"o\".\"measure\" END) +\n      (MAX(\"p\".\"pos\") - MAX(\"p\".\"lower_rn\")) * (\n        MAX(CASE WHEN \"o\".\"rn\" = \"p\".\"upper_rn\" THEN \"o\".\"measure\" END) -\n        MAX(CASE WHEN \"o\".\"rn\" = \"p\".\"lower_rn\" THEN \"o\".\"measure\" END)\n      )\n  END AS \"percentile_measure\"\nFROM \"ordered\" AS \"o\"\nJOIN \"positions\" AS \"p\"\n  ON \"o\".\"class\" = \"p\".\"class\"\nGROUP BY \"o\".\"class\"\nORDER BY \"percentile_measure\" DESC;","notes":"Emulates the template's 90th-percentile grouped point for \"class\" over CAST(\"word_freq_857\" AS REAL), using SQLite window functions and linear interpolation because SQLite does not support PERCENTILE_CONT and the source columns are stored as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ac1ae7d00da00881/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00069bc1bdb3008e93e0c8ef159e3b3b5eee2a91
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.
+Result preview: [{"class": "1", "condition_ratio": 0.0}, {"class": "0", "condition_ratio": null}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..e6b8ec0b3bba90ef3c1e14941b58c120c20e1147
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/generated_sql.sql
@@ -0,0 +1,24 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: contrastive_conditional_view
+-- template_id: tpl_m4_group_ratio_two_conditions
+-- query_record_id: v2q_n1_aede3038f7b5c4f1
+-- problem_id: v2p_n1_e36bbadf54646bf6
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class",
+           SUM(CASE WHEN "class" = '0' THEN 1 ELSE 0 END) AS numerator_count,
+           SUM(CASE WHEN "class" = '1' THEN 1 ELSE 0 END) AS denominator_count
+    FROM "n1"
+    GROUP BY "class"
+)
+SELECT "class",
+       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio
+FROM grouped
+ORDER BY condition_ratio DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1d26fe19f7ae23f24f6157fadc9f41c5fb6aa260
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n    SELECT \"class\",\n           SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT \"class\",\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"class\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\", \"columns\": [\"class\", \"condition_ratio\"], \"rows\": [{\"class\": \"1\", \"condition_ratio\": 0.0}, {\"class\": \"0\", \"condition_ratio\": null}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.79}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd2598910122557fd817c458d746435b9f1cc043
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/run_manifest.json
@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:39:16.921134+00:00",
+  "ended_at": "2026-05-19T15:39:34.104901+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_aede3038f7b5c4f1",
+    "problem_id": "v2p_n1_e36bbadf54646bf6",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "contrastive_conditional_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "condition_col": "class",
+      "condition_value": "0",
+      "positive_value": "0",
+      "negative_value": "1",
+      "top_k": 11,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/1",
+      "binding_index=36"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 4,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_aede3038f7b5c4f1.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_aede3038f7b5c4f1",
+    "api_calls": 0,
+    "input_tokens": 20520,
+    "cached_input_tokens": 19840,
+    "output_tokens": 696,
+    "total_tokens": 21216,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 17171.56,
+    "sql_execution_elapsed_ms_total": 2.79,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7452d8c0bc0c58b44a0c4319e7a6c82aad5b7bb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:39:34.098969+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17171.56, "started_at": "2026-05-19T15:39:16.925782+00:00", "ended_at": "2026-05-19T15:39:34.097393+00:00", "prompt_metrics": {"chars": 30144, "bytes_utf8": 30144, "lines": 795, "estimated_tokens": null}, "response_metrics": {"chars": 656, "bytes_utf8": 656, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 696, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"class\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the provided grouped-ratio template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Since the schema stores values as TEXT, the class labels are compared as '0' and '1'.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..6e3b5ad60ee484312811655c6e4e8017984dd319
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_aede3038f7b5c4f1",
+  "api_calls": 0,
+  "input_tokens": 20520,
+  "cached_input_tokens": 19840,
+  "output_tokens": 696,
+  "total_tokens": 21216,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 17171.56,
+  "sql_execution_elapsed_ms_total": 2.79,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2c57e90a7cdd7d589170061a48e0fcd4c4ff0d9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"word_freq_people": "0", "support": 3749, "avg_response": 0.09191517738063483}, {"word_freq_people": "0.17", "support": 43, "avg_response": 0.0916279069767442}, {"word_freq_people": "0.19", "support": 30, "avg_response": 0.12166666666666666}, {"word_freq_people": "0.3", "support": 29, "avg_response": 0.12103448275862068}, {"word_freq_people": "0.32", "support": 26, "avg_response": 0.030000000000000002}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..5758061ebf6d3271938b77b711dfcc0afb0cc06c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n1_affbd8f7653133c8
+-- problem_id: v2p_n1_e7c97218c147f02c
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "word_freq_people",
+    COUNT(*) AS support,
+    AVG("word_freq_make") AS avg_response
+FROM "n1"
+GROUP BY "word_freq_people"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70e691d3d0371f4dc932d41435a1a22d5e2661d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n1_affbd8f7653133c8\n-- problem_id: v2p_n1_e7c97218c147f02c\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"word_freq_people\",\n    COUNT(*) AS support,\n    AVG(\"word_freq_make\") AS avg_response\nFROM \"n1\"\nGROUP BY \"word_freq_people\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n1_affbd8f7653133c8\\n-- problem_id: v2p_n1_e7c97218c147f02c\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"word_freq_people\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"word_freq_make\\\") AS avg_response\\nFROM \\\"n1\\\"\\nGROUP BY \\\"word_freq_people\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"word_freq_people\", \"support\", \"avg_response\"], \"rows\": [{\"word_freq_people\": \"0\", \"support\": 3749, \"avg_response\": 0.09191517738063483}, {\"word_freq_people\": \"0.17\", \"support\": 43, \"avg_response\": 0.0916279069767442}, {\"word_freq_people\": \"0.19\", \"support\": 30, \"avg_response\": 0.12166666666666666}, {\"word_freq_people\": \"0.3\", \"support\": 29, \"avg_response\": 0.12103448275862068}, {\"word_freq_people\": \"0.32\", \"support\": 26, \"avg_response\": 0.030000000000000002}, {\"word_freq_people\": \"0.27\", \"support\": 20, \"avg_response\": 0.089}, {\"word_freq_people\": \"0.12\", \"support\": 18, \"avg_response\": 0.058333333333333334}, {\"word_freq_people\": \"0.25\", \"support\": 17, \"avg_response\": 0.12588235294117647}, {\"word_freq_people\": \"0.29\", \"support\": 16, \"avg_response\": 0.16125}, {\"word_freq_people\": \"0.2\", \"support\": 16, \"avg_response\": 0.091875}, {\"word_freq_people\": \"0.38\", \"support\": 15, \"avg_response\": 0.5133333333333333}, {\"word_freq_people\": \"0.65\", \"support\": 15, \"avg_response\": 0.3393333333333333}, {\"word_freq_people\": \"0.14\", \"support\": 15, \"avg_response\": 0.09466666666666666}, {\"word_freq_people\": \"0.08\", \"support\": 15, \"avg_response\": 0.06066666666666667}, {\"word_freq_people\": \"0.31\", \"support\": 14, \"avg_response\": 0.305}, {\"word_freq_people\": \"0.11\", \"support\": 14, \"avg_response\": 0.06071428571428572}, {\"word_freq_people\": \"0.16\", \"support\": 13, \"avg_response\": 0.14307692307692307}, {\"word_freq_people\": \"0.54\", \"support\": 12, \"avg_response\": 0.255}, {\"word_freq_people\": \"0.22\", \"support\": 12, \"avg_response\": 0.2041666666666667}, {\"word_freq_people\": \"0.28\", \"support\": 12, \"avg_response\": 0.08666666666666667}, {\"word_freq_people\": \"0.09\", \"support\": 12, \"avg_response\": 0.07583333333333332}, {\"word_freq_people\": \"0.37\", \"support\": 12, \"avg_response\": 0.075}, {\"word_freq_people\": \"0.06\", \"support\": 12, \"avg_response\": 0.06333333333333334}, {\"word_freq_people\": \"0.6\", \"support\": 11, \"avg_response\": 0.34454545454545454}, {\"word_freq_people\": \"0.56\", \"support\": 11, \"avg_response\": 0.28}, {\"word_freq_people\": \"0.15\", \"support\": 11, \"avg_response\": 0.053636363636363635}, {\"word_freq_people\": \"0.4\", \"support\": 10, \"avg_response\": 0.191}, {\"word_freq_people\": \"0.33\", \"support\": 10, \"avg_response\": 0.167}, {\"word_freq_people\": \"0.13\", \"support\": 10, \"avg_response\": 0.128}, {\"word_freq_people\": \"0.1\", \"support\": 10, \"avg_response\": 0.08700000000000001}, {\"word_freq_people\": \"0.42\", \"support\": 9, \"avg_response\": 0.2422222222222222}, {\"word_freq_people\": \"0.55\", \"support\": 9, \"avg_response\": 0.030000000000000002}, {\"word_freq_people\": \"0.66\", \"support\": 8, \"avg_response\": 0.4625}, {\"word_freq_people\": \"0.9\", \"support\": 8, \"avg_response\": 0.43875}, {\"word_freq_people\": \"0.52\", \"support\": 8, \"avg_response\": 0.24375}, {\"word_freq_people\": \"0.21\", \"support\": 8, \"avg_response\": 0.17875}, {\"word_freq_people\": \"0.45\", \"support\": 8, \"avg_response\": 0.1175}, {\"word_freq_people\": \"0.26\", \"support\": 8, \"avg_response\": 0.08875}, {\"word_freq_people\": \"0.87\", \"support\": 7, \"avg_response\": 0.6828571428571429}, {\"word_freq_people\": \"0.93\", \"support\": 7, \"avg_response\": 0.49714285714285716}, {\"word_freq_people\": \"0.58\", \"support\": 7, \"avg_response\": 0.33428571428571424}, {\"word_freq_people\": \"0.62\", \"support\": 7, \"avg_response\": 0.24428571428571427}, {\"word_freq_people\": \"0.57\", \"support\": 7, \"avg_response\": 0.21142857142857144}, {\"word_freq_people\": \"0.46\", \"support\": 7, \"avg_response\": 0.10857142857142857}, {\"word_freq_people\": \"0.47\", \"support\": 7, \"avg_response\": 0.09428571428571429}, {\"word_freq_people\": \"0.59\", \"support\": 6, \"avg_response\": 0.5416666666666666}, {\"word_freq_people\": \"0.79\", \"support\": 6, \"avg_response\": 0.4166666666666667}, {\"word_freq_people\": \"0.61\", \"support\": 6, \"avg_response\": 0.3283333333333333}, {\"word_freq_people\": \"0.41\", \"support\": 6, \"avg_response\": 0.21833333333333335}, {\"word_freq_people\": \"0.23\", \"support\": 6, \"avg_response\": 0.17500000000000002}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.19}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..cc0046cb085b1263d7acca07137cc443a44f4332
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/run_manifest.json
@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.192851+00:00",
+  "ended_at": "2026-05-19T16:06:30.195790+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_affbd8f7653133c8",
+    "problem_id": "v2p_n1_e7c97218c147f02c",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=word_freq_make, key_col=word_freq_people.",
+    "bindings": {
+      "key_col": "word_freq_people",
+      "measure_col": "word_freq_make",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=8",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_affbd8f7653133c8.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_affbd8f7653133c8/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aff9900e0bcbc391cc8f0571e18392ac418225ee
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29769, "bytes_utf8": 29769, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 626, "bytes_utf8": 626, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 692, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..b33e2c71bcc7668646434adb0128cf95477966a6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b3f34675a39f5d77",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 692,
+    "total_tokens": 21129,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13386.89,
+    "sql_execution_elapsed_ms_total": 4.08,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f39ece84e7f0ef2115a5d63962f458fb3dc6920
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:36:46.580352+00:00",
+  "ended_at": "2026-05-19T15:36:59.967265+00:00",
+  "elapsed_ms": 13386.89,
+  "prompt_metrics": {
+    "chars": 29769,
+    "bytes_utf8": 29769,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1018,
+    "bytes_utf8": 1018,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 626,
+      "bytes_utf8": 626,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 12032,
+      "output_tokens": 692,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f85eca2ac16dd5b017a637134ed24dafab81f69
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_labs.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_labs",
+    "item_col": "word_freq_telnet",
+    "top_k": 14,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dfa15abebd50f4968f4017562c22543cdd0e3393
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e1-e62c-7e80-b11a-272a5096cc60"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_telnet\\\",\\n       SUM(CAST(\\\"word_freq_labs\\\" AS REAL)) AS \\\"total_measure\\\",\\n       SUM(CAST(\\\"word_freq_labs\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_labs\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_telnet\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the required template with group_col=\\\"class\\\", item_col=\\\"word_freq_telnet\\\", and measure_col=\\\"word_freq_labs\\\". CAST to REAL is necessary because the schema stores these numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":12032,"output_tokens":692,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7b9d8af3d0e9a99e908c4d2d14886e3ce3cdd98
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_telnet\",\n       SUM(CAST(\"word_freq_labs\" AS REAL)) AS \"total_measure\",\n       SUM(CAST(\"word_freq_labs\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_labs\" AS REAL))) OVER (PARTITION BY \"class\") AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_telnet\"\nORDER BY \"share_within_group\" DESC;","notes":"Used the required template with group_col=\"class\", item_col=\"word_freq_telnet\", and measure_col=\"word_freq_labs\". CAST to REAL is necessary because the schema stores these numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3f34675a39f5d77/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3edf623ae02c23b58fabda72bab0876cb8966104
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.
+Result preview: [{"class": "1", "condition_rate": 1.0}, {"class": "0", "condition_rate": 0.0}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..4a199d2cbea67f2aeca69893a323ef0b20fc5aae
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_n1_b4a50b6eca8328dc
+-- problem_id: v2p_n1_02cce9ed9843f8d4
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class",
+       AVG(CASE WHEN "class" = '1' THEN 1 ELSE 0 END) AS condition_rate
+FROM "n1"
+GROUP BY "class"
+ORDER BY condition_rate DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3f02e98fdc19fcb2f1f5c6f84180ab5e05d55ef
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n       AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"class\", \"condition_rate\"], \"rows\": [{\"class\": \"1\", \"condition_rate\": 1.0}, {\"class\": \"0\", \"condition_rate\": 0.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.19}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..21f5155760c1326b05269030d36b2c3414ac7812
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/run_manifest.json
@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:00:08.582702+00:00",
+  "ended_at": "2026-05-19T16:00:22.870358+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_b4a50b6eca8328dc",
+    "problem_id": "v2p_n1_02cce9ed9843f8d4",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "condition_col": "class",
+      "condition_value": "1",
+      "positive_value": "0",
+      "negative_value": "1",
+      "top_k": 17,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=102"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_b4a50b6eca8328dc.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b4a50b6eca8328dc",
+    "api_calls": 0,
+    "input_tokens": 20372,
+    "cached_input_tokens": 12032,
+    "output_tokens": 329,
+    "total_tokens": 20701,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13273.09,
+    "sql_execution_elapsed_ms_total": 3.19,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb338f03521cb20b515aa25443ab2cfda6a67edd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:00:12.061246+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3473.17, "started_at": "2026-05-19T16:00:08.587190+00:00", "ended_at": "2026-05-19T16:00:12.060393+00:00", "prompt_metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f7-4a99-7380-84f9-6d6ccb26e348\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:00:22.865267+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9799.92, "started_at": "2026-05-19T16:00:13.064384+00:00", "ended_at": "2026-05-19T16:00:22.864357+00:00", "prompt_metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}, "response_metrics": {"chars": 503, "bytes_utf8": 503, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 12032, "output_tokens": 329, "reasoning_output_tokens": 191}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n       AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with group_col=\\\"class\\\" and condition_col=\\\"class\\\" on single table \\\"n1\\\". Since the grouping column and condition column are the same, the result is deterministic by class group: class='1' yields 1.0 and class='0' yields 0.0.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..92ba4618cc1659456c2d55892f6fda3feaaccc85
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_b4a50b6eca8328dc",
+  "api_calls": 0,
+  "input_tokens": 20372,
+  "cached_input_tokens": 12032,
+  "output_tokens": 329,
+  "total_tokens": 20701,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 2,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 13273.09,
+  "sql_execution_elapsed_ms_total": 3.19,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..28cbbcec47ee1c987265691e112c83229c0218b2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29535, "bytes_utf8": 29535, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 957, "bytes_utf8": 957, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20353, "cached_input_tokens": 19840, "output_tokens": 1561, "reasoning_output_tokens": 1263}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..eaa3a6bdd797a05bedcd1a1ef8eff64e8e1d1ab5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b703832fa240aba9",
+    "api_calls": 0,
+    "input_tokens": 20353,
+    "cached_input_tokens": 19840,
+    "output_tokens": 1561,
+    "total_tokens": 21914,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 27746.83,
+    "sql_execution_elapsed_ms_total": 10.42,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f276eaf965086119c8baf56c753f9a469793443a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:52:03.245363+00:00",
+  "ended_at": "2026-05-19T15:52:30.992221+00:00",
+  "elapsed_ms": 27746.83,
+  "prompt_metrics": {
+    "chars": 29535,
+    "bytes_utf8": 29535,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1423,
+    "bytes_utf8": 1423,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 957,
+      "bytes_utf8": 957,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20353,
+      "cached_input_tokens": 19840,
+      "output_tokens": 1561,
+      "reasoning_output_tokens": 1263
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d28dd582ab527be3b9e760b9553c5ba9de6f0b88
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_lab.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_lab",
+    "top_k": 15,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7b6ad4bed8b7a3616caba5714ed17976f402e671
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ef-e2bb-7ba3-8e50-57ae53fd332e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_lab\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_lab\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"class\\\") AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_lab\\\" IS NOT NULL\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    \\\"measure\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"rn\\\" = (\\n    CAST((0.95 * \\\"cnt\\\") AS INT) + CASE\\n      WHEN (0.95 * \\\"cnt\\\") > CAST((0.95 * \\\"cnt\\\") AS INT) THEN 1\\n      ELSE 0\\n    END\\n  )\\n)\\nSELECT\\n  \\\"class\\\",\\n  \\\"measure\\\" AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per \\\"class\\\" with window functions and casts \\\"word_freq_lab\\\" from TEXT to REAL.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20353,"cached_input_tokens":19840,"output_tokens":1561,"reasoning_output_tokens":1263}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a826a0ad83c7376a6d0a5abee0886e22af67fd17
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_lab\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_lab\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"class\") AS \"cnt\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_lab\" IS NOT NULL\n),\n\"picked\" AS (\n  SELECT\n    \"class\",\n    \"measure\"\n  FROM \"ranked\"\n  WHERE \"rn\" = (\n    CAST((0.95 * \"cnt\") AS INT) + CASE\n      WHEN (0.95 * \"cnt\") > CAST((0.95 * \"cnt\") AS INT) THEN 1\n      ELSE 0\n    END\n  )\n)\nSELECT\n  \"class\",\n  \"measure\" AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank 95th percentile per \"class\" with window functions and casts \"word_freq_lab\" from TEXT to REAL."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b703832fa240aba9/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c351316afd51183455bb7c7892ae3440c3366b9b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29334, "bytes_utf8": 29334, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29334, "bytes_utf8": 29334, "lines": 790, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 316, "bytes_utf8": 316, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20321, "cached_input_tokens": 12032, "output_tokens": 377, "reasoning_output_tokens": 281}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e8ed755fc65fb1da3377b9445775b399d6cc9fd7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b71d349558604e39",
+    "api_calls": 0,
+    "input_tokens": 20321,
+    "cached_input_tokens": 12032,
+    "output_tokens": 377,
+    "total_tokens": 20698,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14195.8,
+    "sql_execution_elapsed_ms_total": 2.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b40714c32164f97a1b841c6dbe54b8ddeb1e3173
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:31.629886+00:00",
+  "ended_at": "2026-05-19T16:02:34.858114+00:00",
+  "elapsed_ms": 3228.2,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29334,
+    "bytes_utf8": 29334,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..441425710a4d8d5c9791f3c80d3700867073c736
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:35.861308+00:00",
+  "ended_at": "2026-05-19T16:02:46.828957+00:00",
+  "elapsed_ms": 10967.6,
+  "prompt_metrics": {
+    "chars": 29334,
+    "bytes_utf8": 29334,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 681,
+    "bytes_utf8": 681,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 316,
+      "bytes_utf8": 316,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20321,
+      "cached_input_tokens": 12032,
+      "output_tokens": 377,
+      "reasoning_output_tokens": 281
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..65adaec25f6e959e82ffe34d3228dd0b47d5f3dd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 13,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.16,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..65adaec25f6e959e82ffe34d3228dd0b47d5f3dd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 13,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.16,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d504f9893c020b2c7099a4d9511bc8345afc5ed2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-7944-7572-82ba-8ab4b7ced410"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5d097ad51beb32e6f675c1b15e00e84e4ba7543c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-7944-7572-82ba-8ab4b7ced410"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2beba66aea583b9bc894177d3bb7174617a62903
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-89ec-7f03-b36a-d142320f3f8b"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"class\\\",\\n    COUNT(*) AS \\\"support\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"support\\\" ASC, \\\"class\\\"\\nLIMIT 13;\",\"notes\":\"Applied the Low-Support Group Count template with group_col=\\\"class\\\" and top_k=13 on the single table \\\"n1\\\".\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20321,"cached_input_tokens":12032,"output_tokens":377,"reasoning_output_tokens":281}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..83dab853cfc140ae6ff270b921d12ec33fba4d2c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"class\",\n    COUNT(*) AS \"support\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"support\" ASC, \"class\"\nLIMIT 13;","notes":"Applied the Low-Support Group Count template with group_col=\"class\" and top_k=13 on the single table \"n1\"."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b71d349558604e39/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dddb04d14280454915cf57daa271228451a70069
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29913, "bytes_utf8": 29913, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 670, "bytes_utf8": 670, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 572, "reasoning_output_tokens": 391}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..0ac193a781f18cc3d600d5c0e9b900d33aed0552
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b7d806202770edd7",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 572,
+    "total_tokens": 21025,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18616.53,
+    "sql_execution_elapsed_ms_total": 2.83,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b54b2a8bc913670c87c9ffb47ca0ec8350f580d9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:46:30.403400+00:00",
+  "ended_at": "2026-05-19T15:46:49.019982+00:00",
+  "elapsed_ms": 18616.53,
+  "prompt_metrics": {
+    "chars": 29913,
+    "bytes_utf8": 29913,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1402,
+    "bytes_utf8": 1402,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 670,
+      "bytes_utf8": 670,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20453,
+      "cached_input_tokens": 19840,
+      "output_tokens": 572,
+      "reasoning_output_tokens": 391
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1a393da16fc46f7ca0c6cfbf2c7c14ef7e9b5f05
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
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+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
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+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
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+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
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+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_free.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_free",
+    "top_k": 17,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8399881bc478cdf647f263cda549c8f5523ebac3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40ea-ce83-7b71-b3a8-4594044c4e8d"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 21088. Please try again in 632ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the required Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_free\\\". Cast \\\"word_freq_free\\\" to REAL because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":572,"reasoning_output_tokens":391}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b822c9c92bcdc4c27bd2a54a5cb2cbd27f9395b9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_free\" AS REAL)) AS \"group_value\"\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM grouped\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Used the required Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_free\". Cast \"word_freq_free\" to REAL because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..25fee76508a6b92522c8c68d159647926019e76c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_free.
+Result preview: [{"class": "1", "group_value": 939.79}, {"class": "0", "group_value": 205.16}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..def4f1735e3e0d021fa438a18d052c56b34623c3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_b7d806202770edd7
+-- problem_id: v2p_n1_22fe909d9ca272a4
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_free" AS REAL)) AS "group_value"
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM grouped
+)
+SELECT g."class", g."group_value"
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g."group_value" > t."total_value" * 0.05
+ORDER BY g."group_value" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..831c002d692d1ba4aa2b2fe32b37f7ffc53556f1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_free\" AS REAL)) AS \"group_value\"\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM grouped\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 939.79}, {\"class\": \"0\", \"group_value\": 205.16}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.83}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..669b43e64e1b63fe0d5436a20d17708c51649e9e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:46:30.398568+00:00",
+  "ended_at": "2026-05-19T15:46:49.025862+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_b7d806202770edd7",
+    "problem_id": "v2p_n1_22fe909d9ca272a4",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_free.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_free",
+      "top_k": 17,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=2/2",
+      "binding_index=72"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_b7d806202770edd7.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b7d806202770edd7",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 572,
+    "total_tokens": 21025,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 18616.53,
+    "sql_execution_elapsed_ms_total": 2.83,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..be09a09e3a9cd99f0a3911cd5da8acd96a1f21b6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:46:49.021200+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 18616.53, "started_at": "2026-05-19T15:46:30.403400+00:00", "ended_at": "2026-05-19T15:46:49.019982+00:00", "prompt_metrics": {"chars": 29913, "bytes_utf8": 29913, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 670, "bytes_utf8": 670, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 572, "reasoning_output_tokens": 391}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_free\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Used the required Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_free\\\". Cast \\\"word_freq_free\\\" to REAL because the schema stores feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..bc912f6d5863482ef190c4360fe355107ebbe9a7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_b7d806202770edd7",
+  "api_calls": 0,
+  "input_tokens": 20453,
+  "cached_input_tokens": 19840,
+  "output_tokens": 572,
+  "total_tokens": 21025,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 18616.53,
+  "sql_execution_elapsed_ms_total": 2.83,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7d806202770edd7/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1102db28a0dbf26128ec1e147d98719597c7439
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 293, "bytes_utf8": 293, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20324, "cached_input_tokens": 12032, "output_tokens": 362, "reasoning_output_tokens": 273}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4518f34429afa786caaf2cda7b87f2e62314ac61
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_b7e28565aafa18df",
+    "api_calls": 0,
+    "input_tokens": 20324,
+    "cached_input_tokens": 12032,
+    "output_tokens": 362,
+    "total_tokens": 20686,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9632.62,
+    "sql_execution_elapsed_ms_total": 1.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5047509c615ffcf5d145399102dad930ad8054f1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:37.619812+00:00",
+  "ended_at": "2026-05-19T16:01:47.252460+00:00",
+  "elapsed_ms": 9632.62,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 654,
+    "bytes_utf8": 654,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 293,
+      "bytes_utf8": 293,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20324,
+      "cached_input_tokens": 12032,
+      "output_tokens": 362,
+      "reasoning_output_tokens": 273
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0915c1d3178631d3450ba9980b56c1c4cfa27cb5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
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+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..83e35b8e0712326a19c7392016f018ab89a4a04b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-a66a-7812-a32c-38a95fae867d"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n  \\\"class\\\",\\n  COUNT(*) AS \\\"support\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"support\\\" ASC, \\\"class\\\"\\nLIMIT 10;\",\"notes\":\"Applied the planned Low-Support Group Count template with group_col=\\\"class\\\" and top_k=10.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20324,"cached_input_tokens":12032,"output_tokens":362,"reasoning_output_tokens":273}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..09d519b8818ba5a99e198fc1858578874e03494c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n  \"class\",\n  COUNT(*) AS \"support\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"support\" ASC, \"class\"\nLIMIT 10;","notes":"Applied the planned Low-Support Group Count template with group_col=\"class\" and top_k=10."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b7e28565aafa18df/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..523c33be094a50a939e88faf96d9aa6d86c0d41b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30143, "bytes_utf8": 30143, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 682, "bytes_utf8": 682, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 458, "reasoning_output_tokens": 273}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d2cc7be4eb3dbb5e615e3e9856c984ed6125b7e3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_bcddeaa2e3706681",
+    "api_calls": 0,
+    "input_tokens": 20520,
+    "cached_input_tokens": 19840,
+    "output_tokens": 458,
+    "total_tokens": 20978,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13282.54,
+    "sql_execution_elapsed_ms_total": 5.31,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ae2b5bd5cea7b3e648c87718b9926e9c2963581
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:40:02.791064+00:00",
+  "ended_at": "2026-05-19T15:40:16.073648+00:00",
+  "elapsed_ms": 13282.54,
+  "prompt_metrics": {
+    "chars": 30143,
+    "bytes_utf8": 30143,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1051,
+    "bytes_utf8": 1051,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 682,
+      "bytes_utf8": 682,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20520,
+      "cached_input_tokens": 19840,
+      "output_tokens": 458,
+      "reasoning_output_tokens": 273
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..285c78d393add61f9f7b81bc9addfc4b504c3d5f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
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+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_ratio_two_conditions",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 14,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d3518643d2eccbda8d6dbb3b219d9fecb5c1ae7e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e4-e467-7103-91ec-9f69ae4bcf3f"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"class\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the required grouped-ratio template with both group_col and condition_col bound to \\\"class\\\". Since \\\"class\\\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":19840,"output_tokens":458,"reasoning_output_tokens":273}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..af245f9fd3f9aead8f2b01d6bbdaad57c0116296
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n    SELECT \"class\",\n           SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT \"class\",\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Applied the required grouped-ratio template with both group_col and condition_col bound to \"class\". Since \"class\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bcddeaa2e3706681/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..580f8f6d9c71c274137200032e5bb722ad08407d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.
+Result preview: [{"class": "0", "row_count": 2788}, {"class": "1", "row_count": 1813}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..aa6282ca891009e97383562f2d207123afd95932
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: subgroup_structure
+-- canonical_subitem_id: subgroup_size_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_clickbench_group_count
+-- query_record_id: v2q_n1_bf0b21a004175eea
+-- problem_id: v2p_n1_c4c0125747564b2a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", COUNT(*) AS "row_count"
+FROM "n1"
+GROUP BY "class"
+ORDER BY "row_count" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3edcc390815fcfd1ed4b127fadd6b3e7fc39a32
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"class\", \"row_count\"], \"rows\": [{\"class\": \"0\", \"row_count\": 2788}, {\"class\": \"1\", \"row_count\": 1813}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.51}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..67e3b12db3b488b53221ac64ab188239e677680e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/run_manifest.json
@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:33:02.511821+00:00",
+  "ended_at": "2026-05-19T15:33:11.415594+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_bf0b21a004175eea",
+    "problem_id": "v2p_n1_c4c0125747564b2a",
+    "dataset_id": "n1",
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "subgroup_size_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "top_k": 11,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/1",
+      "binding_index=16"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 2,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_bf0b21a004175eea.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_bf0b21a004175eea",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 12032,
+    "output_tokens": 300,
+    "total_tokens": 20586,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8892.79,
+    "sql_execution_elapsed_ms_total": 3.51,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ebadfdbe399eac33debabf582d4cd7584221dc50
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:33:11.410190+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8892.79, "started_at": "2026-05-19T15:33:02.516303+00:00", "ended_at": "2026-05-19T15:33:11.409129+00:00", "prompt_metrics": {"chars": 29254, "bytes_utf8": 29254, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 295, "bytes_utf8": 295, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 12032, "output_tokens": 300, "reasoning_output_tokens": 221}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the Grouped Count by Category template with \\\"class\\\" as the grouping column to return the count distribution by class.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e94f3d9128659fa4b49f40a6591ce48df9c6e5c4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_bf0b21a004175eea",
+  "api_calls": 0,
+  "input_tokens": 20286,
+  "cached_input_tokens": 12032,
+  "output_tokens": 300,
+  "total_tokens": 20586,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 8892.79,
+  "sql_execution_elapsed_ms_total": 3.51,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..147fba2e13708bb76f8eddf4a512bcdc7b3855d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29468, "bytes_utf8": 29468, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 445, "bytes_utf8": 445, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20329, "cached_input_tokens": 19840, "output_tokens": 268, "reasoning_output_tokens": 162}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..2d7d3e9b00c75d683698342f4480bd1d4d2ef65c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_c321602c561652f8",
+    "api_calls": 0,
+    "input_tokens": 20329,
+    "cached_input_tokens": 19840,
+    "output_tokens": 268,
+    "total_tokens": 20597,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10265.61,
+    "sql_execution_elapsed_ms_total": 15.42,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..ca86425df915983b9efa21dfc61df1696fe44247
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:43:53.846912+00:00",
+  "ended_at": "2026-05-19T15:44:04.112560+00:00",
+  "elapsed_ms": 10265.61,
+  "prompt_metrics": {
+    "chars": 29468,
+    "bytes_utf8": 29468,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 799,
+    "bytes_utf8": 799,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 445,
+      "bytes_utf8": 445,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20329,
+      "cached_input_tokens": 19840,
+      "output_tokens": 268,
+      "reasoning_output_tokens": 162
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..228dc7a977a49ff1178d1a6432fdbaebfbfe2807
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=capital_run_length_average.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "capital_run_length_average",
+    "top_k": 14,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 3.706,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d17390b610ab0cbee2a05fe48de5842416424c88
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e8-6b0c-7212-b8a2-7e51f9413355"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"capital_run_length_average\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the required Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"capital_run_length_average\\\". Cast the measure column to REAL because the schema snapshot shows it stored as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20329,"cached_input_tokens":19840,"output_tokens":268,"reasoning_output_tokens":162}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4585fc75c60f9cddbdb56eb0942bf5610cfc1455
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"capital_run_length_average\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Used the required Window Partition Average template with group_col=\"class\" and measure_col=\"capital_run_length_average\". Cast the measure column to REAL because the schema snapshot shows it stored as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c321602c561652f8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0ecf270f300160cae1184104215a2c74cae2b0c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:54.004536+00:00",
+  "ended_at": "2026-05-19T16:02:56.799871+00:00",
+  "elapsed_ms": 2795.3,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..bccfcdc64631ae7c46b018722494a0025c81c0bb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:57.803244+00:00",
+  "ended_at": "2026-05-19T16:03:01.169487+00:00",
+  "elapsed_ms": 3366.2,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29333,
+    "bytes_utf8": 29333,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdd7ff630e4c682b3a2e120ad9141e6d325da1e3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 14,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdd7ff630e4c682b3a2e120ad9141e6d325da1e3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 14,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e8340a24b8fd64de981963173e7a90410df54a91
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-d0c4-7a42-90cd-92cb4099fbc7"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca21590d0e96ab75f7a9db92dc603d9c903305b7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-d0c4-7a42-90cd-92cb4099fbc7"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f3c99e5a8b0b2dbb122e8ee8c20f982cd9018de1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-dfb7-7601-9719-ecfa46f2aebb"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e19aa825a0b98aea92d0ded04cc541895fe63ee
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-dfb7-7601-9719-ecfa46f2aebb"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c3405917a8d8b1b1/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f51e8d388304c8deeb85234b5e73378c5a95281
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:18.921810+00:00",
+  "ended_at": "2026-05-19T16:04:22.037792+00:00",
+  "elapsed_ms": 3115.95,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29529,
+    "bytes_utf8": 29529,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8e17cc32adda7d3671db7c7ff5c78a5280ae6dd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:23.040039+00:00",
+  "ended_at": "2026-05-19T16:04:26.408245+00:00",
+  "elapsed_ms": 3368.18,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29529,
+    "bytes_utf8": 29529,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8d882797af1b604b12266aec0e8ec43978701e69
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_credit.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_credit",
+    "top_k": 18,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8d882797af1b604b12266aec0e8ec43978701e69
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_credit.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_credit",
+    "top_k": 18,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50eeefc4106460b30306d4f9d67a50188e0d03f8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-1c79-7e13-99c5-642291663c4e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20dfbb6808100cc3b8afc4646dd7aee6f9db398c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-1c79-7e13-99c5-642291663c4e"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..14411fe402c99f4ddf9a41a68b972293fb41b7f9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-2c81-7c63-9ecc-8b156b5f5504"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5931fecd3b271e8528f40afcbf91b1ad115d6107
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-2c81-7c63-9ecc-8b156b5f5504"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_c4efe288202c4a5e/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b22c3b7831895c8c81120560df34d555d4623fe5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29581, "bytes_utf8": 29581, "lines": 795, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 487, "bytes_utf8": 487, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 12032, "output_tokens": 456, "reasoning_output_tokens": 321}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..e2570692ca6695203ecf263a06d87c1f7d372bf8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ca633141a5f00b5c",
+    "api_calls": 0,
+    "input_tokens": 20373,
+    "cached_input_tokens": 12032,
+    "output_tokens": 456,
+    "total_tokens": 20829,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10583.15,
+    "sql_execution_elapsed_ms_total": 3.47,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e2ee8b8eb9be94eebf39b5c8e49003ed9331ef3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:59:02.934600+00:00",
+  "ended_at": "2026-05-19T15:59:13.517775+00:00",
+  "elapsed_ms": 10583.15,
+  "prompt_metrics": {
+    "chars": 29581,
+    "bytes_utf8": 29581,
+    "lines": 795,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 847,
+    "bytes_utf8": 847,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 487,
+      "bytes_utf8": 487,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20373,
+      "cached_input_tokens": 12032,
+      "output_tokens": 456,
+      "reasoning_output_tokens": 321
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd0e4162d9b14cc122a1e9f55f79d6d6f157b275
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,795 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
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+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "class",
+    "condition_col": "class",
+    "condition_value": "1",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 19,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dff443f6df91a2752da86b32893cfeffbafaf62c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-4a24-7301-ae2c-0e8f2db06f5c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n  \\\"class\\\",\\n  AVG(CASE WHEN \\\"class\\\" = '1' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the planned grouped-condition-rate template with both group and condition bound to \\\"class\\\". Since the grouping column and condition column are the same, each group yields a deterministic within-group proportion: 1.0 for class='1' and 0.0 otherwise.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20373,"cached_input_tokens":12032,"output_tokens":456,"reasoning_output_tokens":321}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0c12c5da6ed0e3861b75898b0bf8f0dd2673093
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT\n  \"class\",\n  AVG(CASE WHEN \"class\" = '1' THEN 1.0 ELSE 0.0 END) AS \"condition_rate\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the planned grouped-condition-rate template with both group and condition bound to \"class\". Since the grouping column and condition column are the same, each group yields a deterministic within-group proportion: 1.0 for class='1' and 0.0 otherwise."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ca633141a5f00b5c/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..707d6071328ab84d06eb8391d5c3bd0da77eb027
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29356, "bytes_utf8": 29356, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 392, "bytes_utf8": 392, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20317, "cached_input_tokens": 12032, "output_tokens": 249, "reasoning_output_tokens": 146}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c6c38f8b2cbcfef474405090aa2f663e136617e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ccae8e9460c99cb1",
+    "api_calls": 0,
+    "input_tokens": 20317,
+    "cached_input_tokens": 12032,
+    "output_tokens": 249,
+    "total_tokens": 20566,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 14441.72,
+    "sql_execution_elapsed_ms_total": 3.65,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d9a773bbdd60ebb33a5d6e34219216847d161a7a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:29:57.750631+00:00",
+  "ended_at": "2026-05-19T15:30:12.192377+00:00",
+  "elapsed_ms": 14441.72,
+  "prompt_metrics": {
+    "chars": 29356,
+    "bytes_utf8": 29356,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1078,
+    "bytes_utf8": 1078,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 392,
+      "bytes_utf8": 392,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20317,
+      "cached_input_tokens": 12032,
+      "output_tokens": 249,
+      "reasoning_output_tokens": 146
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2418bc3c2fd23aae3fa98c6d8179c293908ef717
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_our.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_our",
+    "top_k": 19,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.16,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f98dd756a57f670e292f760de0eee61249a35f47
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40db-a90d-7fd2-96fa-dbab5ab03f34"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1979770, Requested 20952. Please try again in 21ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_our\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_our\\\". CAST to REAL is applied because the schema snapshot types these numeric feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20317,"cached_input_tokens":12032,"output_tokens":249,"reasoning_output_tokens":146}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f25a2f8c378f67ad9c892908dbb263de287944c5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_our\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Used the provided Grouped Numeric Sum template with group_col=\"class\" and measure_col=\"word_freq_our\". CAST to REAL is applied because the schema snapshot types these numeric feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ccae8e9460c99cb1/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc43b2efac24ffa40fc8ea44ad005d01a0c690da
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29547, "bytes_utf8": 29547, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1704, "bytes_utf8": 1704, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 2124, "reasoning_output_tokens": 1569}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..56aa9ec1ae2924d2829b7599c5d302e73e5795d3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_cd4fbcfd87bc1668",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 19840,
+    "output_tokens": 2124,
+    "total_tokens": 22479,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 43754.7,
+    "sql_execution_elapsed_ms_total": 19.5,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..acaf737ab349377161663f36a6b3a5ad1b2b4959
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:57.084270+00:00",
+  "ended_at": "2026-05-19T15:57:40.839023+00:00",
+  "elapsed_ms": 43754.7,
+  "prompt_metrics": {
+    "chars": 29547,
+    "bytes_utf8": 29547,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2288,
+    "bytes_utf8": 2288,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1704,
+      "bytes_utf8": 1704,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 19840,
+      "output_tokens": 2124,
+      "reasoning_output_tokens": 1569
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d52a9e283d87b9e00be8f1f147ed84a864e9eda
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_technology.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_technology",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..077e6037cee88437b8904844181d9e4177a981cf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f4-5e94-7f23-97e1-a73fcecc76ef"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n  SELECT\\n    \\\"class\\\" AS \\\"class\\\",\\n    CAST(\\\"word_freq_technology\\\" AS REAL) AS \\\"measure\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_technology\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"class\\\") AS \\\"cnt\\\"\\n  FROM \\\"n1\\\"\\n),\\n\\\"positions\\\" AS (\\n  SELECT\\n    \\\"class\\\" AS \\\"class\\\",\\n    ((0.95 * (MAX(\\\"cnt\\\") - 1)) + 1.0) AS \\\"pos\\\",\\n    CAST(((0.95 * (MAX(\\\"cnt\\\") - 1)) + 1.0) AS INTEGER) AS \\\"lower_rn\\\",\\n    CAST(((0.95 * (MAX(\\\"cnt\\\") - 1)) + 1.0) AS INTEGER) +\\n      CASE\\n        WHEN ((0.95 * (MAX(\\\"cnt\\\") - 1)) + 1.0) > CAST(((0.95 * (MAX(\\\"cnt\\\") - 1)) + 1.0) AS INTEGER) THEN 1\\n        ELSE 0\\n      END AS \\\"upper_rn\\\"\\n  FROM \\\"ordered\\\"\\n  GROUP BY \\\"class\\\"\\n),\\n\\\"percentiles\\\" AS (\\n  SELECT\\n    p.\\\"class\\\" AS \\\"class\\\",\\n    CASE\\n      WHEN p.\\\"lower_rn\\\" = p.\\\"upper_rn\\\" THEN l.\\\"measure\\\"\\n      ELSE l.\\\"measure\\\" + (p.\\\"pos\\\" - p.\\\"lower_rn\\\") * (u.\\\"measure\\\" - l.\\\"measure\\\")\\n    END AS \\\"percentile_measure\\\"\\n  FROM \\\"positions\\\" AS p\\n  JOIN \\\"ordered\\\" AS l\\n    ON l.\\\"class\\\" = p.\\\"class\\\"\\n   AND l.\\\"rn\\\" = p.\\\"lower_rn\\\"\\n  JOIN \\\"ordered\\\" AS u\\n    ON u.\\\"class\\\" = p.\\\"class\\\"\\n   AND u.\\\"rn\\\" = p.\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"class\\\",\\n  \\\"percentile_measure\\\"\\nFROM \\\"percentiles\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this query emulates the 0.95 continuous percentile of \\\"word_freq_technology\\\" within each \\\"class\\\" using window functions and linear interpolation. CAST to REAL is required because the schema stores numeric values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":2124,"reasoning_output_tokens":1569}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3c30e214db9f9037fe49b938502344dd6e79815e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n  SELECT\n    \"class\" AS \"class\",\n    CAST(\"word_freq_technology\" AS REAL) AS \"measure\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_technology\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"class\") AS \"cnt\"\n  FROM \"n1\"\n),\n\"positions\" AS (\n  SELECT\n    \"class\" AS \"class\",\n    ((0.95 * (MAX(\"cnt\") - 1)) + 1.0) AS \"pos\",\n    CAST(((0.95 * (MAX(\"cnt\") - 1)) + 1.0) AS INTEGER) AS \"lower_rn\",\n    CAST(((0.95 * (MAX(\"cnt\") - 1)) + 1.0) AS INTEGER) +\n      CASE\n        WHEN ((0.95 * (MAX(\"cnt\") - 1)) + 1.0) > CAST(((0.95 * (MAX(\"cnt\") - 1)) + 1.0) AS INTEGER) THEN 1\n        ELSE 0\n      END AS \"upper_rn\"\n  FROM \"ordered\"\n  GROUP BY \"class\"\n),\n\"percentiles\" AS (\n  SELECT\n    p.\"class\" AS \"class\",\n    CASE\n      WHEN p.\"lower_rn\" = p.\"upper_rn\" THEN l.\"measure\"\n      ELSE l.\"measure\" + (p.\"pos\" - p.\"lower_rn\") * (u.\"measure\" - l.\"measure\")\n    END AS \"percentile_measure\"\n  FROM \"positions\" AS p\n  JOIN \"ordered\" AS l\n    ON l.\"class\" = p.\"class\"\n   AND l.\"rn\" = p.\"lower_rn\"\n  JOIN \"ordered\" AS u\n    ON u.\"class\" = p.\"class\"\n   AND u.\"rn\" = p.\"upper_rn\"\n)\nSELECT\n  \"class\",\n  \"percentile_measure\"\nFROM \"percentiles\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this query emulates the 0.95 continuous percentile of \"word_freq_technology\" within each \"class\" using window functions and linear interpolation. CAST to REAL is required because the schema stores numeric values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cd4fbcfd87bc1668/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..64a7a6a071752953f68be36f22c4d8f501d2017c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/final_answer.txt
@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"value_label": "0", "support": 2788, "support_share": 0.6059552271245382, "support_rank": 1}, {"value_label": "1", "support": 1813, "support_share": 0.39404477287546186, "support_rank": 2}]}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..2faf80cea9a558d263eae0f08424867a3a5648b1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/generated_sql.sql
@@ -0,0 +1,25 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: support_concentration
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_cardinality_support_rank_profile
+-- query_record_id: v2q_n1_ce5f903c51e57c45
+-- problem_id: v2p_n1_df477c14c94f32d1
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "class" AS value_label, COUNT(*) AS support
+    FROM "n1"
+    GROUP BY "class"
+)
+SELECT
+    value_label,
+    support,
+    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank
+FROM grouped
+ORDER BY support DESC, value_label;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e23dbf155d0b221bdd85dd9fcb8791fdebc39bb2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/query_results.jsonl
@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: n1\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_n1_ce5f903c51e57c45\n-- problem_id: v2p_n1_df477c14c94f32d1\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n    SELECT \"class\" AS value_label, COUNT(*) AS support\n    FROM \"n1\"\n    GROUP BY \"class\"\n)\nSELECT\n    value_label,\n    support,\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: n1\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_n1_ce5f903c51e57c45\\n-- problem_id: v2p_n1_df477c14c94f32d1\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n    SELECT \\\"class\\\" AS value_label, COUNT(*) AS support\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n)\\nSELECT\\n    value_label,\\n    support,\\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"0\", \"support\": 2788, \"support_share\": 0.6059552271245382, \"support_rank\": 1}, {\"value_label\": \"1\", \"support\": 1813, \"support_share\": 0.39404477287546186, \"support_rank\": 2}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.38}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..9ffdde5c97d09e9de09a7772519baec1dfd2edec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/run_manifest.json
@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:30.161698+00:00",
+  "ended_at": "2026-05-19T16:06:30.164950+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_ce5f903c51e57c45",
+    "problem_id": "v2p_n1_df477c14c94f32d1",
+    "dataset_id": "n1",
+    "template_id": "tpl_cardinality_support_rank_profile",
+    "template_name": "Cardinality Support Rank Profile",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "support_rank_profile_consistency",
+    "intended_facet_id": "support_concentration",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class"
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col} AS value_label, COUNT(*) AS support\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT\n    value_label,\n    support,\n    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;",
+    "notes": [
+      "default_facets=support_concentration,value_imbalance_profile",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_ce5f903c51e57c45.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce5f903c51e57c45/usage_summary.json
@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..83e4b088cdde35c7e15f198c7255487d2acf5735
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:22.874131+00:00",
+  "ended_at": "2026-05-19T16:00:26.452123+00:00",
+  "elapsed_ms": 3577.97,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29299,
+    "bytes_utf8": 29299,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..950ebad96a35854c7e43abc259b306b4c75d465b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:00:27.455125+00:00",
+  "ended_at": "2026-05-19T16:00:30.770608+00:00",
+  "elapsed_ms": 3315.44,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29299,
+    "bytes_utf8": 29299,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57590f7d6108714264c73b124805a89d861e34b4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%21.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "char_freq_%21",
+    "top_k": 13,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.315,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57590f7d6108714264c73b124805a89d861e34b4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%21.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "char_freq_%21",
+    "top_k": 13,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.315,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3fc11114ccbff3ef14282903ace50c1a69ea10cb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-8277-7612-8574-ecb760072225"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..385e2625dd655a8fb7a3847887ad6f7639d39bcc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-8277-7612-8574-ecb760072225"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f50ff7c255bd34cc49ef646b27ed1758c8a09713
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-944d-76d2-b39d-f6d8209b362d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc32a72d875062a09756ddc9af61b3795ff69620
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-944d-76d2-b39d-f6d8209b362d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cfb81ca0cd14c086/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..58c24fdcb2865b345a0d2194d3f7872800d60910
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%5B.
+Result preview: [{"class": "0", "avg_measure": 0.02268364418938307}, {"class": "1", "avg_measure": 0.008198565912851627}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b086d07f3038463b2e6936f5b565bf82cb12968a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_cff363bb65d6bf4b
+-- problem_id: v2p_n1_c48dc19efe6edd0b
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("char_freq_%5B" AS REAL)) OVER (PARTITION BY "class") AS "avg_measure"
+FROM "n1"
+ORDER BY "avg_measure" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c12fd56d6ce53094c03346b51091368abdc76658
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%5B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.02268364418938307}, {\"class\": \"1\", \"avg_measure\": 0.008198565912851627}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.75}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c4af0bdfdbe7d69e924f953f3d6efa23749a233c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:42:27.594002+00:00",
+  "ended_at": "2026-05-19T15:42:41.588409+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_cff363bb65d6bf4b",
+    "problem_id": "v2p_n1_c48dc19efe6edd0b",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%5B.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%5B",
+      "top_k": 15,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=2/2",
+      "binding_index=50"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_cff363bb65d6bf4b.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_cff363bb65d6bf4b",
+    "api_calls": 0,
+    "input_tokens": 20330,
+    "cached_input_tokens": 19840,
+    "output_tokens": 511,
+    "total_tokens": 20841,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13980.04,
+    "sql_execution_elapsed_ms_total": 8.75,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7629f6ba88b08205f1549ad473160cd6f21d2ab
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:42:41.578188+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13980.04, "started_at": "2026-05-19T15:42:27.597240+00:00", "ended_at": "2026-05-19T15:42:41.577314+00:00", "prompt_metrics": {"chars": 29439, "bytes_utf8": 29439, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 442, "bytes_utf8": 442, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 19840, "output_tokens": 511, "reasoning_output_tokens": 395}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Used the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%5B\\\". CAST to REAL is included because the schema snapshot shows numeric features stored as TEXT in SQLite.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..079e889f534e85b4d3d9ff2bfa459f173d022392
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_cff363bb65d6bf4b",
+  "api_calls": 0,
+  "input_tokens": 20330,
+  "cached_input_tokens": 19840,
+  "output_tokens": 511,
+  "total_tokens": 20841,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 13980.04,
+  "sql_execution_elapsed_ms_total": 8.75,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..66ba6f147cffd4632373d6efec79fdacd2153195
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29442, "bytes_utf8": 29442, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 397, "bytes_utf8": 397, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 19840, "output_tokens": 247, "reasoning_output_tokens": 142}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab5c79234aaa94d6e45609c0cbcf64e0bcb91fa5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d0837298f0ad430d",
+    "api_calls": 0,
+    "input_tokens": 20328,
+    "cached_input_tokens": 19840,
+    "output_tokens": 247,
+    "total_tokens": 20575,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 27194.38,
+    "sql_execution_elapsed_ms_total": 8.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0c39b66026426a18b2a3ce838193bbc2d1734867
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:41:50.377303+00:00",
+  "ended_at": "2026-05-19T15:42:17.571727+00:00",
+  "elapsed_ms": 27194.38,
+  "prompt_metrics": {
+    "chars": 29442,
+    "bytes_utf8": 29442,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2417,
+    "bytes_utf8": 2417,
+    "lines": 9,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 397,
+      "bytes_utf8": 397,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20328,
+      "cached_input_tokens": 19840,
+      "output_tokens": 247,
+      "reasoning_output_tokens": 142
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47cc4f0837e316fe2f11c4b2944ef63c30ed1ccb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%28.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%28",
+    "top_k": 19,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.136,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2558c55adf22b3b3ceb1af397111afbd24a827df
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,9 @@
+{"type":"thread.started","thread_id":"019e40e6-88b1-79b1-b521-ae8e5e4ed3c9"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1995988, Requested 20963. Please try again in 508ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20963. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20963. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20963. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 1/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1989919, Requested 20963. Please try again in 326ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%28\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%28\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20328,"cached_input_tokens":19840,"output_tokens":247,"reasoning_output_tokens":142}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87caf13630901241f279c848e4d164d1606d9fb6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%28\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%28\". CAST to REAL is used because the schema stores feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..992bfbea3ef5bcf3b21c3e1b11652ccb4251ad89
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%28.
+Result preview: [{"class": "0", "avg_measure": 0.15857819225251077}, {"class": "1", "avg_measure": 0.10897021511307226}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..aa8dbc078fd7880bb5daad3f3665db95476265c9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/generated_sql.sql
@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_d0837298f0ad430d
+-- problem_id: v2p_n1_646d2451fd36ca80
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("char_freq_%28" AS REAL)) OVER (PARTITION BY "class") AS avg_measure
+FROM "n1"
+ORDER BY avg_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f27efc0bfa165f1ad981088b43e178b6e5947ac5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"class\",\n  AVG(CAST(\"char_freq_%28\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%28\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.15857819225251077}, {\"class\": \"1\", \"avg_measure\": 0.10897021511307226}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.23}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..0da66e47aaa3381fc1d31aca8458b5d6433930c3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:41:50.373103+00:00",
+  "ended_at": "2026-05-19T15:42:17.582886+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_d0837298f0ad430d",
+    "problem_id": "v2p_n1_646d2451fd36ca80",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%28.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%28",
+      "top_k": 19,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.136,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=2/2",
+      "binding_index=49"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_d0837298f0ad430d.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d0837298f0ad430d",
+    "api_calls": 0,
+    "input_tokens": 20328,
+    "cached_input_tokens": 19840,
+    "output_tokens": 247,
+    "total_tokens": 20575,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 27194.38,
+    "sql_execution_elapsed_ms_total": 8.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..75ef2a2b5056480a8c2aa3902e52ca93123c848f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:42:17.572812+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 27194.38, "started_at": "2026-05-19T15:41:50.377303+00:00", "ended_at": "2026-05-19T15:42:17.571727+00:00", "prompt_metrics": {"chars": 29442, "bytes_utf8": 29442, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 397, "bytes_utf8": 397, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 19840, "output_tokens": 247, "reasoning_output_tokens": 142}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%28\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%28\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..168e19c4397585263b0f4c5a124d17fa2028f9bb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_d0837298f0ad430d",
+  "api_calls": 0,
+  "input_tokens": 20328,
+  "cached_input_tokens": 19840,
+  "output_tokens": 247,
+  "total_tokens": 20575,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 27194.38,
+  "sql_execution_elapsed_ms_total": 8.23,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d0837298f0ad430d/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e03a01f4b9b0a885dff8692d2f3d3c156889fb08
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 642, "bytes_utf8": 642, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 651, "reasoning_output_tokens": 472}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4730e133c9d9a4d2893b9978c13b0e68f0b93ffa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d1094c91a9c3d755",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 651,
+    "total_tokens": 21104,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 20191.78,
+    "sql_execution_elapsed_ms_total": 2.61,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e2f457f92978af3cc2dc3bee6dc49b20965ad21
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:49:59.983706+00:00",
+  "ended_at": "2026-05-19T15:50:20.175520+00:00",
+  "elapsed_ms": 20191.78,
+  "prompt_metrics": {
+    "chars": 29910,
+    "bytes_utf8": 29910,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1707,
+    "bytes_utf8": 1707,
+    "lines": 6,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 642,
+      "bytes_utf8": 642,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20453,
+      "cached_input_tokens": 19840,
+      "output_tokens": 651,
+      "reasoning_output_tokens": 472
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..335d2f5ab0cac8759aa7968ada6ae62c13b53537
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_000.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_000",
+    "top_k": 19,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e08e14872a7a26c484768de61960babdd4aae302
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,6 @@
+{"type":"thread.started","thread_id":"019e40ee-0150-72b3-88a6-fe82239ca9ed"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 21088. Please try again in 632ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 21088. Please try again in 632ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_000\\\". Cast \\\"word_freq_000\\\" to REAL because the schema stores numeric-looking values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":651,"reasoning_output_tokens":472}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..164e52ea5cafc27946e2fb7b5d52b5bbcd2adb4b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_000\" AS REAL)) AS \"group_value\"\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM grouped\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;","notes":"Applied the required template with group_col=\"class\" and measure_col=\"word_freq_000\". Cast \"word_freq_000\" to REAL because the schema stores numeric-looking values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..71ff08d3241e0525758eb3b99668fff18cd37b17
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_000.
+Result preview: [{"class": "1", "group_value": 447.91}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..51ff45d4de5d2709a5a157ea7646a65ca7d1351d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_d1094c91a9c3d755
+-- problem_id: v2p_n1_4216999ae5b3df44
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_000" AS REAL)) AS "group_value"
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM grouped
+)
+SELECT g."class", g."group_value"
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g."group_value" > t."total_value" * 0.05
+ORDER BY g."group_value" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3740a13e6cfd577f94a0cd1b8b63af6b5277cb3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_000\" AS REAL)) AS \"group_value\"\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(\"group_value\") AS \"total_value\"\n    FROM grouped\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 447.91}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.61}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ade272ddec1170ff685780907471b082b307d5e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:49:59.981025+00:00",
+  "ended_at": "2026-05-19T15:50:20.180705+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_d1094c91a9c3d755",
+    "problem_id": "v2p_n1_4216999ae5b3df44",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_000.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_000",
+      "top_k": 19,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=79"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_d1094c91a9c3d755.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d1094c91a9c3d755",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 651,
+    "total_tokens": 21104,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 20191.78,
+    "sql_execution_elapsed_ms_total": 2.61,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3d9193dca5a06795521e56a3560ff089db9a12d3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:50:20.176699+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 20191.78, "started_at": "2026-05-19T15:49:59.983706+00:00", "ended_at": "2026-05-19T15:50:20.175520+00:00", "prompt_metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 642, "bytes_utf8": 642, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 651, "reasoning_output_tokens": 472}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS \\\"group_value\\\"\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_000\\\". Cast \\\"word_freq_000\\\" to REAL because the schema stores numeric-looking values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..512b631935f3c5eab905ffd4f8982d0dcd4d3e21
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_d1094c91a9c3d755",
+  "api_calls": 0,
+  "input_tokens": 20453,
+  "cached_input_tokens": 19840,
+  "output_tokens": 651,
+  "total_tokens": 21104,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 20191.78,
+  "sql_execution_elapsed_ms_total": 2.61,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d1094c91a9c3d755/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37a5e47bfe0d25fcf6caba791036694e5776fb28
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/conversation.jsonl
@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29521, "bytes_utf8": 29521, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29521, "bytes_utf8": 29521, "lines": 792, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 463, "bytes_utf8": 463, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20362, "cached_input_tokens": 12032, "output_tokens": 394, "reasoning_output_tokens": 264}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..bec04812f8e58b9bb4d1c139cf511ff8ac60b628
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 2,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d11cd2b20b3ef4c7",
+    "api_calls": 0,
+    "input_tokens": 20362,
+    "cached_input_tokens": 12032,
+    "output_tokens": 394,
+    "total_tokens": 20756,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15333.9,
+    "sql_execution_elapsed_ms_total": 5.17,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..822cbd53458cd29905577709a3f0c33fa9106cb5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:05:03.725400+00:00",
+  "ended_at": "2026-05-19T16:05:07.174970+00:00",
+  "elapsed_ms": 3449.53,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29521,
+    "bytes_utf8": 29521,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b019370e7df6b8ac27fabbc7779229fe2e5e7fd3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:05:08.177488+00:00",
+  "ended_at": "2026-05-19T16:05:20.061897+00:00",
+  "elapsed_ms": 11884.37,
+  "prompt_metrics": {
+    "chars": 29521,
+    "bytes_utf8": 29521,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 831,
+    "bytes_utf8": 831,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 463,
+      "bytes_utf8": 463,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20362,
+      "cached_input_tokens": 12032,
+      "output_tokens": 394,
+      "reasoning_output_tokens": 264
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..449df4ad9da97542f58197ee2a07c57f5bdc9526
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_000.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_000",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..449df4ad9da97542f58197ee2a07c57f5bdc9526
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_000.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_000",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de261bf003bb0f281b11fe3e8d6abe910f223624
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-cb6a-7e90-942a-b49cc926c69d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61c3bcd2394f64a3f75669d6b4b257d85915f408
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-cb6a-7e90-942a-b49cc926c69d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..09bedc12001b91fa722eedc4c75b5b88b5cf0fb2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-dcd6-70a3-bc96-9fc340e6aecf"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_000\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 11;\",\"notes\":\"Used the planned Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_000\\\". CAST to REAL is included because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20362,"cached_input_tokens":12032,"output_tokens":394,"reasoning_output_tokens":264}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0f87a858a66ed33948c1d599605fc369c9aa3147
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_response_attempt_2.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_000\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_000\" AS REAL)) > 0.0\nORDER BY \"total_measure\" DESC\nLIMIT 11;","notes":"Used the planned Thresholded Group Ranking template with group_col=\"class\" and measure_col=\"word_freq_000\". CAST to REAL is included because the schema stores feature columns as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d11cd2b20b3ef4c7/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..77ca937ec1064b46fe291501084eb1a118bd5dba
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29363, "bytes_utf8": 29363, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 393, "bytes_utf8": 393, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20317, "cached_input_tokens": 12032, "output_tokens": 478, "reasoning_output_tokens": 372}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c230f680bf1eba53f760a0b7985db1d5d7b8b5d1
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d19c57eec440c0d2",
+    "api_calls": 0,
+    "input_tokens": 20317,
+    "cached_input_tokens": 12032,
+    "output_tokens": 478,
+    "total_tokens": 20795,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 22014.09,
+    "sql_execution_elapsed_ms_total": 4.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb4b36daa230b9da8a4e389d0f5a675188030d34
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:31:40.997965+00:00",
+  "ended_at": "2026-05-19T15:32:03.012113+00:00",
+  "elapsed_ms": 22014.09,
+  "prompt_metrics": {
+    "chars": 29363,
+    "bytes_utf8": 29363,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1756,
+    "bytes_utf8": 1756,
+    "lines": 7,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 393,
+      "bytes_utf8": 393,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20317,
+      "cached_input_tokens": 12032,
+      "output_tokens": 478,
+      "reasoning_output_tokens": 372
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2076b0187a257b945e8d74c0a4ecbf9567ee817
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_internet.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_internet",
+    "top_k": 12,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb8bfd7c751830c21f83e87a3864dfe1e4c66fa3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,7 @@
+{"type":"thread.started","thread_id":"019e40dd-3c51-7940-a26b-85f593b45cc2"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20952. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20952. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1986294, Requested 20952. Please try again in 217ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n  \\\"class\\\",\\n  SUM(CAST(\\\"word_freq_internet\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the provided grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_internet\\\". Cast the measure to REAL because the schema snapshot shows TEXT storage.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20317,"cached_input_tokens":12032,"output_tokens":478,"reasoning_output_tokens":372}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e85da331a9b4e70b56d58ff29112620d2d8fd7d2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT\n  \"class\",\n  SUM(CAST(\"word_freq_internet\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Used the provided grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_internet\". Cast the measure to REAL because the schema snapshot shows TEXT storage."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d19c57eec440c0d2/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..7705643030a0eb5680123c27b7f30b45e68476d5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:06:23.192842+00:00",
+  "ended_at": "2026-05-19T16:06:26.099154+00:00",
+  "elapsed_ms": 2906.29,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29523,
+    "bytes_utf8": 29523,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f6c208ef3194961b8e52823c323fe90252df9054
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:06:27.102239+00:00",
+  "ended_at": "2026-05-19T16:06:30.155584+00:00",
+  "elapsed_ms": 3053.3,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29523,
+    "bytes_utf8": 29523,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eb046f15e814693544dbd01671f0a31d8764c015
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_hpl.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hpl",
+    "top_k": 19,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eb046f15e814693544dbd01671f0a31d8764c015
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_hpl.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hpl",
+    "top_k": 19,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5381a88020fdc7fc29df1491518bbeb938757e76
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fd-01d6-7620-8dc0-de386e0a4800"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9a970e25a5f220ee55abf1a689e0caca3084d63
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fd-01d6-7620-8dc0-de386e0a4800"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..24f00836e5a467cc2d02d7d74c42528977be3f89
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fd-1114-7bb3-a267-d06af0399af7"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cd6a5521c3a085c7e605c29577da2bc1ea97a361
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fd-1114-7bb3-a267-d06af0399af7"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..4902411b6d9d12d4cdfc4a8de42f711a59bb8e84
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/run_manifest.json
@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:06:23.190417+00:00",
+  "ended_at": "2026-05-19T16:06:30.156602+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_d2f49a7ad34ed30a",
+    "problem_id": "v2p_n1_d1ec536e9e8cfe66",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_hpl.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_hpl",
+      "top_k": 19,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=139"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed08ad715b3eac6c31719d9a2f029c42e4368987
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d2f49a7ad34ed30a/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:06:26.099933+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2906.29, "started_at": "2026-05-19T16:06:23.192842+00:00", "ended_at": "2026-05-19T16:06:26.099154+00:00", "prompt_metrics": {"chars": 29523, "bytes_utf8": 29523, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-01d6-7620-8dc0-de386e0a4800\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:06:30.156499+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3053.3, "started_at": "2026-05-19T16:06:27.102239+00:00", "ended_at": "2026-05-19T16:06:30.155584+00:00", "prompt_metrics": {"chars": 29523, "bytes_utf8": 29523, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-1114-7bb3-a267-d06af0399af7\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af770cc5b3f40cf0844f21c9293eebdb6e8a03de
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 423, "bytes_utf8": 423, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20331, "cached_input_tokens": 19840, "output_tokens": 263, "reasoning_output_tokens": 156}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..0ec374ac3d07cf4e5e97b327fc5c9ef6955502db
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d8e143029f8f8f1b",
+    "api_calls": 0,
+    "input_tokens": 20331,
+    "cached_input_tokens": 19840,
+    "output_tokens": 263,
+    "total_tokens": 20594,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9983.01,
+    "sql_execution_elapsed_ms_total": 19.61,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..70f735e006b8b6b1a0157b0d68cd98216e7129e7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:42:17.585404+00:00",
+  "ended_at": "2026-05-19T15:42:27.568454+00:00",
+  "elapsed_ms": 9983.01,
+  "prompt_metrics": {
+    "chars": 29440,
+    "bytes_utf8": 29440,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 777,
+    "bytes_utf8": 777,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 423,
+      "bytes_utf8": 423,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20331,
+      "cached_input_tokens": 19840,
+      "output_tokens": 263,
+      "reasoning_output_tokens": 156
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..385c9f63a898211ba13fa2f9d30b85c85f678331
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%5B.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "char_freq_%5B",
+    "top_k": 10,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0626d0970bdf128d3a7cb4b77f2a8c53f7fcbb20
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e6-f2ff-7710-8a98-58748f0aa421"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%5B\\\". Cast the measure to REAL because the schema snapshot stores numeric features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20331,"cached_input_tokens":19840,"output_tokens":263,"reasoning_output_tokens":156}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b7a73e433ba3bd4830164639e3fa6389f7543c7f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%5B\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Applied the provided Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%5B\". Cast the measure to REAL because the schema snapshot stores numeric features as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..89b5e111a8ba382b4cd6c1bb704246f5c9eacb6f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%5B.
+Result preview: [{"class": "0", "avg_measure": 0.02268364418938307}, {"class": "1", "avg_measure": 0.008198565912851627}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..eb3972c119c07941fbccf05df37f38f6dde4361d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/generated_sql.sql
@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_d8e143029f8f8f1b
+-- problem_id: v2p_n1_baeff616a5a496a3
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "class",
+       AVG(CAST("char_freq_%5B" AS REAL)) OVER (PARTITION BY "class") AS avg_measure
+FROM "n1"
+ORDER BY avg_measure DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3405cf4b4f2345bb3fa41c7ecb8e1147bb83fc9a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n       AVG(CAST(\"char_freq_%5B\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.02268364418938307}, {\"class\": \"1\", \"avg_measure\": 0.008198565912851627}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 19.61}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1bbe36fdcd1ed1e6d664d837a60703bfa2e5ab8b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:42:17.583438+00:00",
+  "ended_at": "2026-05-19T15:42:27.593018+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_d8e143029f8f8f1b",
+    "problem_id": "v2p_n1_baeff616a5a496a3",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%5B.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%5B",
+      "top_k": 10,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=50"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_d8e143029f8f8f1b.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_d8e143029f8f8f1b",
+    "api_calls": 0,
+    "input_tokens": 20331,
+    "cached_input_tokens": 19840,
+    "output_tokens": 263,
+    "total_tokens": 20594,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9983.01,
+    "sql_execution_elapsed_ms_total": 19.61,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3488dfdc1e1e76c5aad87d08f05314bc7ad2b8e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:42:27.570130+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9983.01, "started_at": "2026-05-19T15:42:17.585404+00:00", "ended_at": "2026-05-19T15:42:27.568454+00:00", "prompt_metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 423, "bytes_utf8": 423, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20331, "cached_input_tokens": 19840, "output_tokens": 263, "reasoning_output_tokens": 156}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n       AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%5B\\\". Cast the measure to REAL because the schema snapshot stores numeric features as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5ead2dec42597ead8858109cbb508e82706b9be
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_d8e143029f8f8f1b",
+  "api_calls": 0,
+  "input_tokens": 20331,
+  "cached_input_tokens": 19840,
+  "output_tokens": 263,
+  "total_tokens": 20594,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 9983.01,
+  "sql_execution_elapsed_ms_total": 19.61,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d8e143029f8f8f1b/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..648c3e5a6659b715608b3177e2844dfdf7f22655
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_415.
+Result preview: [{"class": "1", "word_freq_85": "0", "total_measure": 3.22, "share_within_group": 100.0}, {"class": "0", "word_freq_85": "4.76", "total_measure": 28.56, "share_within_group": 13.169179692903583}, {"class": "0", "word_freq_85": "0", "total_measure": 20.07, "share_within_group": 9.254392032092959}, {"class": "0", "word_freq_85": "4.34", "total_measure": 13.02, "share_within_group": 6.003596624706045}, {"class": "0", "word_freq_85": "4.16", "total_measure": 12.48, "share_within_group": 5.754599529672154}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b70e7d84e5e878400d4e3b8ff28a240d755f3417
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/generated_sql.sql
@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_dad817b1a18d3020
+-- problem_id: v2p_n1_b504a7c52e6b3139
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", "word_freq_85",
+       SUM(CAST("word_freq_415" AS REAL)) AS total_measure,
+       SUM(CAST("word_freq_415" AS REAL)) * 100.0 / SUM(SUM(CAST("word_freq_415" AS REAL))) OVER (PARTITION BY "class") AS share_within_group
+FROM "n1"
+GROUP BY "class", "word_freq_85"
+ORDER BY share_within_group DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dea3384176d6ca6222c3c8c1596d0c349414a047
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_85\",\n       SUM(CAST(\"word_freq_415\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_415\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_415\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_85\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_85\\\",\\n       SUM(CAST(\\\"word_freq_415\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_415\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_415\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_85\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"class\", \"word_freq_85\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_85\": \"0\", \"total_measure\": 3.22, \"share_within_group\": 100.0}, {\"class\": \"0\", \"word_freq_85\": \"4.76\", \"total_measure\": 28.56, \"share_within_group\": 13.169179692903583}, {\"class\": \"0\", \"word_freq_85\": \"0\", \"total_measure\": 20.07, \"share_within_group\": 9.254392032092959}, {\"class\": \"0\", \"word_freq_85\": \"4.34\", \"total_measure\": 13.02, \"share_within_group\": 6.003596624706045}, {\"class\": \"0\", \"word_freq_85\": \"4.16\", \"total_measure\": 12.48, \"share_within_group\": 5.754599529672154}, {\"class\": \"0\", \"word_freq_85\": \"2.04\", \"total_measure\": 4.76, \"share_within_group\": 2.194863282150597}, {\"class\": \"0\", \"word_freq_85\": \"2.32\", \"total_measure\": 4.64, \"share_within_group\": 2.139530594365288}, {\"class\": \"0\", \"word_freq_85\": \"4.54\", \"total_measure\": 4.54, \"share_within_group\": 2.093420021210864}, {\"class\": \"0\", \"word_freq_85\": \"4\", \"total_measure\": 4.0, \"share_within_group\": 1.8444229261769722}, {\"class\": \"0\", \"word_freq_85\": \"3.84\", \"total_measure\": 3.84, \"share_within_group\": 1.7706460091298935}, {\"class\": \"0\", \"word_freq_85\": \"3.57\", \"total_measure\": 3.57, \"share_within_group\": 1.646147461612948}, {\"class\": \"0\", \"word_freq_85\": \"1.72\", \"total_measure\": 3.44, \"share_within_group\": 1.5862037165121963}, {\"class\": \"0\", \"word_freq_85\": \"0.58\", \"total_measure\": 3.19, \"share_within_group\": 1.4709272836261353}, {\"class\": \"0\", \"word_freq_85\": \"1.58\", \"total_measure\": 3.17, \"share_within_group\": 1.4617051689952505}, {\"class\": \"0\", \"word_freq_85\": \"3.12\", \"total_measure\": 3.12, \"share_within_group\": 1.4386498824180385}, {\"class\": \"0\", \"word_freq_85\": \"3.03\", \"total_measure\": 3.03, \"share_within_group\": 1.3971503665790566}, {\"class\": \"0\", \"word_freq_85\": \"2.77\", \"total_measure\": 2.77, \"share_within_group\": 1.2772628763775533}, {\"class\": \"0\", \"word_freq_85\": \"2.63\", \"total_measure\": 2.63, \"share_within_group\": 1.2127080739613594}, {\"class\": \"0\", \"word_freq_85\": \"1.31\", \"total_measure\": 2.62, \"share_within_group\": 1.2080970166459168}, {\"class\": \"0\", \"word_freq_85\": \"0.86\", \"total_measure\": 2.58, \"share_within_group\": 1.1896527873841471}, {\"class\": \"0\", \"word_freq_85\": \"1.28\", \"total_measure\": 2.56, \"share_within_group\": 1.1804306727532623}, {\"class\": \"0\", \"word_freq_85\": \"2.56\", \"total_measure\": 2.56, \"share_within_group\": 1.1804306727532623}, {\"class\": \"0\", \"word_freq_85\": \"1.2\", \"total_measure\": 2.4, \"share_within_group\": 1.1066537557061833}, {\"class\": \"0\", \"word_freq_85\": \"0.76\", \"total_measure\": 2.2800000000000002, \"share_within_group\": 1.0513210679208744}, {\"class\": \"0\", \"word_freq_85\": \"2.27\", \"total_measure\": 2.27, \"share_within_group\": 1.046710010605432}, {\"class\": \"0\", \"word_freq_85\": \"2.22\", \"total_measure\": 2.22, \"share_within_group\": 1.0236547240282199}, {\"class\": \"0\", \"word_freq_85\": \"0.5\", \"total_measure\": 2.17, \"share_within_group\": 1.0005994374510074}, {\"class\": \"0\", \"word_freq_85\": \"1.08\", \"total_measure\": 2.16, \"share_within_group\": 0.995988380135565}, {\"class\": \"0\", \"word_freq_85\": \"0.68\", \"total_measure\": 2.04, \"share_within_group\": 0.9406556923502559}, {\"class\": \"0\", \"word_freq_85\": \"1.01\", \"total_measure\": 2.02, \"share_within_group\": 0.9314335777193711}, {\"class\": \"0\", \"word_freq_85\": \"2\", \"total_measure\": 2.0, \"share_within_group\": 0.9222114630884861}, {\"class\": \"0\", \"word_freq_85\": \"0.66\", \"total_measure\": 1.98, \"share_within_group\": 0.9129893484576013}, {\"class\": \"0\", \"word_freq_85\": \"0.63\", \"total_measure\": 1.8900000000000001, \"share_within_group\": 0.8714898326186195}, {\"class\": \"0\", \"word_freq_85\": \"0.93\", \"total_measure\": 1.86, \"share_within_group\": 0.8576566606722922}, {\"class\": \"0\", \"word_freq_85\": \"1.85\", \"total_measure\": 1.85, \"share_within_group\": 0.8530456033568498}, {\"class\": \"0\", \"word_freq_85\": \"1.75\", \"total_measure\": 1.74, \"share_within_group\": 0.802323972886983}, {\"class\": \"0\", \"word_freq_85\": \"0.42\", \"total_measure\": 1.69, \"share_within_group\": 0.7792686863097709}, {\"class\": \"0\", \"word_freq_85\": \"0.55\", \"total_measure\": 1.6500000000000001, \"share_within_group\": 0.7608244570480011}, {\"class\": \"0\", \"word_freq_85\": \"0.54\", \"total_measure\": 1.62, \"share_within_group\": 0.7469912851016738}, {\"class\": \"0\", \"word_freq_85\": \"0.64\", \"total_measure\": 1.6, \"share_within_group\": 0.7377691704707889}, {\"class\": \"0\", \"word_freq_85\": \"0.8\", \"total_measure\": 1.6, \"share_within_group\": 0.7377691704707889}, {\"class\": \"0\", \"word_freq_85\": \"0.39\", \"total_measure\": 1.56, \"share_within_group\": 0.7193249412090192}, {\"class\": \"0\", \"word_freq_85\": \"1.56\", \"total_measure\": 1.56, \"share_within_group\": 0.7193249412090192}, {\"class\": \"0\", \"word_freq_85\": \"0.73\", \"total_measure\": 1.46, \"share_within_group\": 0.6732143680545949}, {\"class\": \"0\", \"word_freq_85\": \"1.44\", \"total_measure\": 1.44, \"share_within_group\": 0.66399225342371}, {\"class\": \"0\", \"word_freq_85\": \"1.42\", \"total_measure\": 1.42, \"share_within_group\": 0.6547701387928252}, {\"class\": \"0\", \"word_freq_85\": \"0.92\", \"total_measure\": 1.38, \"share_within_group\": 0.6363259095310555}, {\"class\": \"0\", \"word_freq_85\": \"1.38\", \"total_measure\": 1.38, \"share_within_group\": 0.6363259095310555}, {\"class\": \"0\", \"word_freq_85\": \"0.88\", \"total_measure\": 1.32, \"share_within_group\": 0.6086595656384008}, {\"class\": \"0\", \"word_freq_85\": \"0.65\", \"total_measure\": 1.3, \"share_within_group\": 0.599437451007516}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 9.44}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..d62146c21c64bec7554644abe03e222dab2e2ee5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:39:04.957795+00:00",
+  "ended_at": "2026-05-19T15:39:16.919843+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_dad817b1a18d3020",
+    "problem_id": "v2p_n1_b504a7c52e6b3139",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_415.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_415",
+      "item_col": "word_freq_85",
+      "top_k": 18,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=10",
+      "sql_variant_index=2/2",
+      "binding_index=33"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 10,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_dad817b1a18d3020.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_dad817b1a18d3020",
+    "api_calls": 0,
+    "input_tokens": 20438,
+    "cached_input_tokens": 12032,
+    "output_tokens": 621,
+    "total_tokens": 21059,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11945.66,
+    "sql_execution_elapsed_ms_total": 9.44,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73bdb1410c25c5d5e01cb28c2ef267306895e85d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:39:16.906965+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11945.66, "started_at": "2026-05-19T15:39:04.959664+00:00", "ended_at": "2026-05-19T15:39:16.905353+00:00", "prompt_metrics": {"chars": 29770, "bytes_utf8": 29770, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 621, "bytes_utf8": 621, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 12032, "output_tokens": 621, "reasoning_output_tokens": 444}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_85\\\",\\n       SUM(CAST(\\\"word_freq_415\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_415\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_415\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_85\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_85\\\", and measure_col=\\\"word_freq_415\\\". Cast the measure from TEXT to REAL because the schema stores numeric fields as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4aa3dec93ecc7f4281484a05090b1b5b7ab29662
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_dad817b1a18d3020",
+  "api_calls": 0,
+  "input_tokens": 20438,
+  "cached_input_tokens": 12032,
+  "output_tokens": 621,
+  "total_tokens": 21059,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11945.66,
+  "sql_execution_elapsed_ms_total": 9.44,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57812e907bcf791fea9617d0ac9bb792522d5c99
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29774, "bytes_utf8": 29774, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 684, "bytes_utf8": 684, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 19840, "output_tokens": 729, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..7bd2a3bcd5118b19b2fb4bf0f7737f8daf9ed6eb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_db1e3de48debfbb8",
+    "api_calls": 0,
+    "input_tokens": 20438,
+    "cached_input_tokens": 19840,
+    "output_tokens": 729,
+    "total_tokens": 21167,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15405.6,
+    "sql_execution_elapsed_ms_total": 4.84,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1bc03883c7920acd5891fbfe47b2dfac520fba8b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:34:40.232535+00:00",
+  "ended_at": "2026-05-19T15:34:55.638164+00:00",
+  "elapsed_ms": 15405.6,
+  "prompt_metrics": {
+    "chars": 29774,
+    "bytes_utf8": 29774,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1068,
+    "bytes_utf8": 1068,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 684,
+      "bytes_utf8": 684,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20438,
+      "cached_input_tokens": 19840,
+      "output_tokens": 729,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1d7e45b1414f560918ba66e0201ca9975cab109
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_hpl.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hpl",
+    "item_col": "word_freq_george",
+    "top_k": 15,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a733af6e033c3d14ed957c0a64897cea7fe3224
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40df-f862-7712-b79a-4124684b8480"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_george\\\",\\n  SUM(COALESCE(CAST(\\\"word_freq_hpl\\\" AS REAL), 0.0)) AS total_measure,\\n  SUM(COALESCE(CAST(\\\"word_freq_hpl\\\" AS REAL), 0.0)) * 100.0\\n    / SUM(SUM(COALESCE(CAST(\\\"word_freq_hpl\\\" AS REAL), 0.0))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_george\\\"\\nORDER BY share_within_group DESC\\nLIMIT 15;\",\"notes\":\"Uses the requested Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_george\\\", and measure_col=\\\"word_freq_hpl\\\". CAST to REAL is included because the schema stores these fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20438,"cached_input_tokens":19840,"output_tokens":729,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ce495e6c3e4d61c3fc340dd6af7410cc40f05de3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_george\",\n  SUM(COALESCE(CAST(\"word_freq_hpl\" AS REAL), 0.0)) AS total_measure,\n  SUM(COALESCE(CAST(\"word_freq_hpl\" AS REAL), 0.0)) * 100.0\n    / SUM(SUM(COALESCE(CAST(\"word_freq_hpl\" AS REAL), 0.0))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_george\"\nORDER BY share_within_group DESC\nLIMIT 15;","notes":"Uses the requested Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_george\", and measure_col=\"word_freq_hpl\". CAST to REAL is included because the schema stores these fields as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db1e3de48debfbb8/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d957f3165879354ee8ba758343cac948010db09e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:16.446326+00:00",
+  "ended_at": "2026-05-19T15:56:19.665727+00:00",
+  "elapsed_ms": 3219.38,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e104afb5599a787dd77c8a7d57486e4bdf6de95
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:20.668815+00:00",
+  "ended_at": "2026-05-19T15:56:23.694332+00:00",
+  "elapsed_ms": 3025.47,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29533,
+    "bytes_utf8": 29533,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4582851815ce51ccbcaa74d540793e0ddfc19bca
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_415.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_415",
+    "top_k": 15,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4582851815ce51ccbcaa74d540793e0ddfc19bca
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_415.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_415",
+    "top_k": 15,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..65f6686c90b6e56730fecc46eafb3ae3db09b71a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-bfc9-7a01-886b-3394fc6c6666"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0cad1748430235811dee05b77443e4e64c31615f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-bfc9-7a01-886b-3394fc6c6666"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0642e0483a6ef29753e23bb3ccc23bec2485f636
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-d04a-7892-82a4-8575fcfc42cd"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3392c0ba7e53c8e681cba4de8a01daf49c70be19
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-d04a-7892-82a4-8575fcfc42cd"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dc908ca51b8f3688/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..d187e23b36a8d973869a9c00e18d88861e91e404
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:26.411557+00:00",
+  "ended_at": "2026-05-19T16:04:29.693276+00:00",
+  "elapsed_ms": 3281.7,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29524,
+    "bytes_utf8": 29524,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_2.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..7b9b055735c6e0c39111b2fd34ecaefef676569b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_attempt_2.metadata.json
@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:04:30.695311+00:00",
+  "ended_at": "2026-05-19T16:04:33.535533+00:00",
+  "elapsed_ms": 2840.19,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29524,
+    "bytes_utf8": 29524,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a5d85dc7fa9d47f543c2642189f5b7899f348eca
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_your.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_your",
+    "top_k": 14,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 1.27,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a5d85dc7fa9d47f543c2642189f5b7899f348eca
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_prompt_attempt_2.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_your.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_your",
+    "top_k": 14,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 1.27,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1c226785d0fab0bcf7a7c284eb46139ab07a934
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-39ae-7911-9411-28ec0161727f"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3e2b510d8b3034f67641761ad221ef505a0f39dd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_1.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-39ae-7911-9411-28ec0161727f"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2e74734f659d225a871ce3e4bf54bbcd5f6b5ebf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-4a5b-7fd1-b9a7-dcd6197e8c3d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e856cda9ae81e2d54a081b29432726eb75e61a43
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_response_attempt_2.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-4a5b-7fd1-b9a7-dcd6197e8c3d"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/cli/sql_stderr_attempt_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1256ae3551f2c7485f19a944656142cf4f210507
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/run_manifest.json
@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:04:26.409492+00:00",
+  "ended_at": "2026-05-19T16:04:33.536425+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_dcca918c0e5d9a78",
+    "problem_id": "v2p_n1_665103cb839f9fe1",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_your.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_your",
+      "top_k": 14,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 1.27,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=134"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ffa97148a2aae5745956f314c246bc5601106e7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dcca918c0e5d9a78/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:04:29.693968+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3281.7, "started_at": "2026-05-19T16:04:26.411557+00:00", "ended_at": "2026-05-19T16:04:29.693276+00:00", "prompt_metrics": {"chars": 29524, "bytes_utf8": 29524, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-39ae-7911-9411-28ec0161727f\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:04:33.536334+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2840.19, "started_at": "2026-05-19T16:04:30.695311+00:00", "ended_at": "2026-05-19T16:04:33.535533+00:00", "prompt_metrics": {"chars": 29524, "bytes_utf8": 29524, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-4a5b-7fd1-b9a7-dcd6197e8c3d\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..628706a4553210e9e8685b1e0f7530db67c28ade
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/run_manifest.json
@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:03:33.752312+00:00",
+  "ended_at": "2026-05-19T16:03:40.563731+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_df35e0cdf57f0221",
+    "problem_id": "v2p_n1_2fb812ac7f86dac9",
+    "dataset_id": "n1",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.",
+    "bindings": {
+      "group_col": "class",
+      "top_k": 11,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=1/2",
+      "binding_index=126"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e348868f731175d92c5e33bf8bc3e932b65d126a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:03:36.517064+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2761.91, "started_at": "2026-05-19T16:03:33.754336+00:00", "ended_at": "2026-05-19T16:03:36.516268+00:00", "prompt_metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fa-6c0d-7b21-9bf0-6175a5685077\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:03:40.563643+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3043.4, "started_at": "2026-05-19T16:03:37.519398+00:00", "ended_at": "2026-05-19T16:03:40.562847+00:00", "prompt_metrics": {"chars": 29333, "bytes_utf8": 29333, "lines": 790, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fa-7ab5-7ac3-9517-92338750e78c\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7159e5cdf1d9baa01a639b32b400016faa56209f
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29534, "bytes_utf8": 29534, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 535, "bytes_utf8": 535, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20370, "cached_input_tokens": 12032, "output_tokens": 623, "reasoning_output_tokens": 473}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d55e061275b7b8459950b8552175dc70c2247a5a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_e03bb83ed3925779",
+    "api_calls": 0,
+    "input_tokens": 20370,
+    "cached_input_tokens": 12032,
+    "output_tokens": 623,
+    "total_tokens": 20993,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12344.91,
+    "sql_execution_elapsed_ms_total": 6.39,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..33f8654e214dca44a9502ab2ff1a66180e5fa15d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:45:45.992043+00:00",
+  "ended_at": "2026-05-19T15:45:58.337014+00:00",
+  "elapsed_ms": 12344.91,
+  "prompt_metrics": {
+    "chars": 29534,
+    "bytes_utf8": 29534,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 914,
+    "bytes_utf8": 914,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 535,
+      "bytes_utf8": 535,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20370,
+      "cached_input_tokens": 12032,
+      "output_tokens": 623,
+      "reasoning_output_tokens": 473
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..816ca064ce365198019b66b0764edeaa08c4e4bd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_order.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "word_freq_order",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52f6a9d8a0c0259fdd57e37efa0814d5aecf8190
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ea-2104-7401-9482-4ca20ad6d8da"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n    SELECT CAST(\\\"word_freq_order\\\" AS REAL) AS \\\"word_freq_order\\\",\\n           NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_order\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n    FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_order\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_order\\\" DESC;\",\"notes\":\"Used the requested Quantile Tail Slice template on \\\"word_freq_order\\\" and cast the TEXT-typed feature to REAL so SQLite buckets and sorts it numerically.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20370,"cached_input_tokens":12032,"output_tokens":623,"reasoning_output_tokens":473}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..63ada454d2a75c3c84fbaded65911fdbb3249f42
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n    SELECT CAST(\"word_freq_order\" AS REAL) AS \"word_freq_order\",\n           NTILE(10) OVER (ORDER BY CAST(\"word_freq_order\" AS REAL) DESC) AS \"tail_bucket\"\n    FROM \"n1\"\n)\nSELECT \"word_freq_order\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_order\" DESC;","notes":"Used the requested Quantile Tail Slice template on \"word_freq_order\" and cast the TEXT-typed feature to REAL so SQLite buckets and sorts it numerically."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e03bb83ed3925779/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6f50458ed498a905f1c5c9229f4e2f6695aadaa
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29912, "bytes_utf8": 29912, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 607, "bytes_utf8": 607, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 593, "reasoning_output_tokens": 433}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..32ff4c7bd9733f268a6c642b3ec2ef34610f93a9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_e0f0a8545a81cb6c",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 19840,
+    "output_tokens": 593,
+    "total_tokens": 21044,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15587.56,
+    "sql_execution_elapsed_ms_total": 2.48,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..cf96e7b33ae6df46504870c0eb1cd9e2852d1eb5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:50:20.183314+00:00",
+  "ended_at": "2026-05-19T15:50:35.770893+00:00",
+  "elapsed_ms": 15587.56,
+  "prompt_metrics": {
+    "chars": 29912,
+    "bytes_utf8": 29912,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 974,
+    "bytes_utf8": 974,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 607,
+      "bytes_utf8": 607,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20451,
+      "cached_input_tokens": 19840,
+      "output_tokens": 593,
+      "reasoning_output_tokens": 433
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0170d31a7271e27bae45f75d344e2959df16922b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_money.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_money",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d7c21185b95d75640cda0f12923a3a36dc7a4bf2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ee-5026-7071-9887-efe9c589c16a"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the planned template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_money\\\". CAST to REAL is applied because the schema snapshot stores numeric fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":19840,"output_tokens":593,"reasoning_output_tokens":433}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..959d4c02f56d481ed08f348e13f55bc23a1e7034
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Used the planned template with group_col=\"class\" and measure_col=\"word_freq_money\". CAST to REAL is applied because the schema snapshot stores numeric fields as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e0f0a8545a81cb6c/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ec8964fade77eaf00ea8ddb1496064e1e0a31ec
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_email.
+Result preview: [{"class": "1", "group_value": 578.76}, {"class": "0", "group_value": 271.25}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..7d39553516dab25f00c73ea171dae941e944dd36
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_e73d9afbaad490eb
+-- problem_id: v2p_n1_374870e264df01fb
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+  SELECT "class", SUM(CAST("word_freq_email" AS REAL)) AS "group_value"
+  FROM "n1"
+  GROUP BY "class"
+), "total" AS (
+  SELECT SUM("group_value") AS "total_value"
+  FROM "grouped"
+)
+SELECT g."class", g."group_value"
+FROM "grouped" AS g
+CROSS JOIN "total" AS t
+WHERE g."group_value" > t."total_value" * 0.1
+ORDER BY g."group_value" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d875d9fad7f9c80fa7e355cac67e787d8e7cb935
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n  SELECT \"class\", SUM(CAST(\"word_freq_email\" AS REAL)) AS \"group_value\"\n  FROM \"n1\"\n  GROUP BY \"class\"\n), \"total\" AS (\n  SELECT SUM(\"group_value\") AS \"total_value\"\n  FROM \"grouped\"\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n  SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_email\\\" AS REAL)) AS \\\"group_value\\\"\\n  FROM \\\"n1\\\"\\n  GROUP BY \\\"class\\\"\\n), \\\"total\\\" AS (\\n  SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n  FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 578.76}, {\"class\": \"0\", \"group_value\": 271.25}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 6.02}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..1140be899a0a8561397f04d137d04c7288887836
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:47:14.990967+00:00",
+  "ended_at": "2026-05-19T15:47:34.030381+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_e73d9afbaad490eb",
+    "problem_id": "v2p_n1_374870e264df01fb",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_email.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_email",
+      "top_k": 14,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=74"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_e73d9afbaad490eb.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_e73d9afbaad490eb",
+    "api_calls": 0,
+    "input_tokens": 20451,
+    "cached_input_tokens": 12032,
+    "output_tokens": 504,
+    "total_tokens": 20955,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 19024.65,
+    "sql_execution_elapsed_ms_total": 6.02,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..092c332f660522aa3d49db3ba8b11b99e621003d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:47:34.020941+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 19024.65, "started_at": "2026-05-19T15:47:14.994095+00:00", "ended_at": "2026-05-19T15:47:34.018799+00:00", "prompt_metrics": {"chars": 29912, "bytes_utf8": 29912, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 669, "bytes_utf8": 669, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 504, "reasoning_output_tokens": 312}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n  SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_email\\\" AS REAL)) AS \\\"group_value\\\"\\n  FROM \\\"n1\\\"\\n  GROUP BY \\\"class\\\"\\n), \\\"total\\\" AS (\\n  SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n  FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the requested Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_email\\\". Casts \\\"word_freq_email\\\" from TEXT to REAL for numeric aggregation.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..ac1adffe47cca33d7408b7c89e45d57038b2caa6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_e73d9afbaad490eb",
+  "api_calls": 0,
+  "input_tokens": 20451,
+  "cached_input_tokens": 12032,
+  "output_tokens": 504,
+  "total_tokens": 20955,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 19024.65,
+  "sql_execution_elapsed_ms_total": 6.02,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a49c0634d8ccaaefd93f930788f7e1f1b370a414
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29254, "bytes_utf8": 29254, "lines": 790, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 273, "bytes_utf8": 273, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 304, "reasoning_output_tokens": 226}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..89f5bb5e1977d7bdf5a45a1f2e02b1e0cc2c799a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_ef17ac560c2ae5db",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 19840,
+    "output_tokens": 304,
+    "total_tokens": 20590,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8800.83,
+    "sql_execution_elapsed_ms_total": 1.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2b61664373d8cafacf27e19cfd1498f139ac8ca8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:32:12.119237+00:00",
+  "ended_at": "2026-05-19T15:32:20.920101+00:00",
+  "elapsed_ms": 8800.83,
+  "prompt_metrics": {
+    "chars": 29254,
+    "bytes_utf8": 29254,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 631,
+    "bytes_utf8": 631,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 273,
+      "bytes_utf8": 273,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20286,
+      "cached_input_tokens": 19840,
+      "output_tokens": 304,
+      "reasoning_output_tokens": 226
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cd0465cd94e4f6fbe7e453549899d411df786486
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,790 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c677e3ef37428835237c46c2a26a6d4919cf18c5
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40dd-b5ee-7952-9170-bd3fc69d6d13"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the required grouped-count template with group_col bound to \\\"class\\\" on the single table \\\"n1\\\".\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":19840,"output_tokens":304,"reasoning_output_tokens":226}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..82e543bf8c9d63fda0b64f1fc4fe7eaf435b19fb
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;","notes":"Uses the required grouped-count template with group_col bound to \"class\" on the single table \"n1\"."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ef17ac560c2ae5db/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0b2078ea88503fe9a0cb72d753d2ad8e7bfd59c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29772, "bytes_utf8": 29772, "lines": 794, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 653, "bytes_utf8": 653, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 709, "reasoning_output_tokens": 516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..48acf99bc29843037d21be218e49a6047b30a4f0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f158fa312fc49119",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 709,
+    "total_tokens": 21146,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 16651.53,
+    "sql_execution_elapsed_ms_total": 4.03,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0fd2369d61def61d38e77a527085ad7db6129986
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:38:06.103555+00:00",
+  "ended_at": "2026-05-19T15:38:22.755122+00:00",
+  "elapsed_ms": 16651.53,
+  "prompt_metrics": {
+    "chars": 29772,
+    "bytes_utf8": 29772,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1048,
+    "bytes_utf8": 1048,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 653,
+      "bytes_utf8": 653,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 12032,
+      "output_tokens": 709,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..564055ce62cc4a1873661ee12a20d6b9805f4565
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,794 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
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+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
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+      "word_freq_85": "0",
+      "word_freq_technology": "0",
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+      "word_freq_parts": "0",
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+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
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+      "word_freq_money": "0.06",
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+      "word_freq_george": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
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+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
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+      "word_freq_addresses": "0",
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+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
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+      "word_freq_money": "0",
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+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
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+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
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+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
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+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_857.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_857",
+    "item_col": "word_freq_data",
+    "top_k": 16,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ae22154cc81ab2e7e5ebb2f642263c962f0509a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e3-1c8e-7390-94b3-a8cc79a1565f"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_data\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) * 100.0 /\\n    NULLIF(SUM(SUM(CAST(\\\"word_freq_857\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0) AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_data\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_data\\\", and measure_col=\\\"word_freq_857\\\". CAST to REAL is used because the schema stores these fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":12032,"output_tokens":709,"reasoning_output_tokens":516}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8bb24a1ad404a25ba93d0fe62b124fde4e29579a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_data\",\n  SUM(CAST(\"word_freq_857\" AS REAL)) AS \"total_measure\",\n  SUM(CAST(\"word_freq_857\" AS REAL)) * 100.0 /\n    NULLIF(SUM(SUM(CAST(\"word_freq_857\" AS REAL))) OVER (PARTITION BY \"class\"), 0) AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_data\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the planned Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_data\", and measure_col=\"word_freq_857\". CAST to REAL is used because the schema stores these fields as TEXT in SQLite."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ee19e2a19164fe7201ccc2df0cc1d5140cd1038
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_857.
+Result preview: [{"class": "1", "word_freq_data": "0", "total_measure": 0.94, "share_within_group": 100.0}, {"class": "0", "word_freq_data": "0", "total_measure": 207.79, "share_within_group": 96.40885259592632}, {"class": "0", "word_freq_data": "1.2", "total_measure": 1.2, "share_within_group": 0.556767039391268}, {"class": "0", "word_freq_data": "1.76", "total_measure": 0.58, "share_within_group": 0.2691040690391128}, {"class": "0", "word_freq_data": "0.55", "total_measure": 0.55, "share_within_group": 0.25518489305433123}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..78e1250dabf3e1e20d7c1340ad21fa33fd1e9096
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/generated_sql.sql
@@ -0,0 +1,22 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_f158fa312fc49119
+-- problem_id: v2p_n1_997002dda6df513e
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "class",
+  "word_freq_data",
+  SUM(CAST("word_freq_857" AS REAL)) AS "total_measure",
+  SUM(CAST("word_freq_857" AS REAL)) * 100.0 /
+    NULLIF(SUM(SUM(CAST("word_freq_857" AS REAL))) OVER (PARTITION BY "class"), 0) AS "share_within_group"
+FROM "n1"
+GROUP BY "class", "word_freq_data"
+ORDER BY "share_within_group" DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..018e588f18e75d0aa966d170d665338ed0598105
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_data\",\n  SUM(CAST(\"word_freq_857\" AS REAL)) AS \"total_measure\",\n  SUM(CAST(\"word_freq_857\" AS REAL)) * 100.0 /\n    NULLIF(SUM(SUM(CAST(\"word_freq_857\" AS REAL))) OVER (PARTITION BY \"class\"), 0) AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_data\"\nORDER BY \"share_within_group\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_data\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) * 100.0 /\\n    NULLIF(SUM(SUM(CAST(\\\"word_freq_857\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0) AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_data\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\", \"columns\": [\"class\", \"word_freq_data\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_data\": \"0\", \"total_measure\": 0.94, \"share_within_group\": 100.0}, {\"class\": \"0\", \"word_freq_data\": \"0\", \"total_measure\": 207.79, \"share_within_group\": 96.40885259592632}, {\"class\": \"0\", \"word_freq_data\": \"1.2\", \"total_measure\": 1.2, \"share_within_group\": 0.556767039391268}, {\"class\": \"0\", \"word_freq_data\": \"1.76\", \"total_measure\": 0.58, \"share_within_group\": 0.2691040690391128}, {\"class\": \"0\", \"word_freq_data\": \"0.55\", \"total_measure\": 0.55, \"share_within_group\": 0.25518489305433123}, {\"class\": \"0\", \"word_freq_data\": \"1.88\", \"total_measure\": 0.53, \"share_within_group\": 0.24590544239781004}, {\"class\": \"0\", \"word_freq_data\": \"0.35\", \"total_measure\": 0.52, \"share_within_group\": 0.24126571706954947}, {\"class\": \"0\", \"word_freq_data\": \"0.24\", \"total_measure\": 0.48, \"share_within_group\": 0.22270681575650722}, {\"class\": \"0\", \"word_freq_data\": \"0.39\", \"total_measure\": 0.39, \"share_within_group\": 0.1809492878021621}, {\"class\": \"0\", \"word_freq_data\": \"0.33\", \"total_measure\": 0.33, \"share_within_group\": 0.15311093583259872}, {\"class\": \"0\", \"word_freq_data\": \"1.34\", \"total_measure\": 0.33, \"share_within_group\": 0.15311093583259872}, {\"class\": \"0\", \"word_freq_data\": \"0.64\", \"total_measure\": 0.32, \"share_within_group\": 0.14847121050433815}, {\"class\": \"0\", \"word_freq_data\": \"0.14\", \"total_measure\": 0.28, \"share_within_group\": 0.1299123091912959}, {\"class\": \"0\", \"word_freq_data\": \"0.57\", \"total_measure\": 0.28, \"share_within_group\": 0.1299123091912959}, {\"class\": \"0\", \"word_freq_data\": \"1.3\", \"total_measure\": 0.26, \"share_within_group\": 0.12063285853477473}, {\"class\": \"0\", \"word_freq_data\": \"2.83\", \"total_measure\": 0.25, \"share_within_group\": 0.11599313320651418}, {\"class\": \"0\", \"word_freq_data\": \"0.37\", \"total_measure\": 0.24, \"share_within_group\": 0.11135340787825361}, {\"class\": \"0\", \"word_freq_data\": \"0.41\", \"total_measure\": 0.2, \"share_within_group\": 0.09279450656521133}, {\"class\": \"0\", \"word_freq_data\": \"0.79\", \"total_measure\": 0.19, \"share_within_group\": 0.08815478123695077}, {\"class\": \"0\", \"word_freq_data\": \"0.17\", \"total_measure\": 0.17, \"share_within_group\": 0.07887533058042964}, {\"class\": \"0\", \"word_freq_data\": \"0.3\", \"total_measure\": 0.15, \"share_within_group\": 0.0695958799239085}, {\"class\": \"0\", \"word_freq_data\": \"0.45\", \"total_measure\": 0.15, \"share_within_group\": 0.0695958799239085}, {\"class\": \"0\", \"word_freq_data\": \"0.46\", \"total_measure\": 0.15, \"share_within_group\": 0.0695958799239085}, {\"class\": \"0\", \"word_freq_data\": \"0.7\", \"total_measure\": 0.1, \"share_within_group\": 0.04639725328260567}, {\"class\": \"0\", \"word_freq_data\": \"1.12\", \"total_measure\": 0.09, \"share_within_group\": 0.041757527954345106}, {\"class\": \"0\", \"word_freq_data\": \"0.03\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.04\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.05\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.06\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.07\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.08\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.09\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.1\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.11\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.12\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.13\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.15\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.16\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.18\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.2\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.21\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.22\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.23\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.25\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.26\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.27\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.28\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.29\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.31\", \"total_measure\": 0.0, \"share_within_group\": 0.0}, {\"class\": \"0\", \"word_freq_data\": \"0.32\", \"total_measure\": 0.0, \"share_within_group\": 0.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 4.03}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..0c91a666e766e292d262138f545fffb02016cdb2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:38:06.099411+00:00",
+  "ended_at": "2026-05-19T15:38:22.761527+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_f158fa312fc49119",
+    "problem_id": "v2p_n1_997002dda6df513e",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_857.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_857",
+      "item_col": "word_freq_data",
+      "top_k": 16,
+      "top_n": 7,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=31"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_f158fa312fc49119.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f158fa312fc49119",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 709,
+    "total_tokens": 21146,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 16651.53,
+    "sql_execution_elapsed_ms_total": 4.03,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e77fe938a076d0eb122e7850b76bb6e602bd62c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:38:22.755946+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16651.53, "started_at": "2026-05-19T15:38:06.103555+00:00", "ended_at": "2026-05-19T15:38:22.755122+00:00", "prompt_metrics": {"chars": 29772, "bytes_utf8": 29772, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 653, "bytes_utf8": 653, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 709, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_data\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) AS \\\"total_measure\\\",\\n  SUM(CAST(\\\"word_freq_857\\\" AS REAL)) * 100.0 /\\n    NULLIF(SUM(SUM(CAST(\\\"word_freq_857\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0) AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_data\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_data\\\", and measure_col=\\\"word_freq_857\\\". CAST to REAL is used because the schema stores these fields as TEXT in SQLite.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..dcaf4e8d0329268229c6b9037d0b452f1535b0dd
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_f158fa312fc49119",
+  "api_calls": 0,
+  "input_tokens": 20437,
+  "cached_input_tokens": 12032,
+  "output_tokens": 709,
+  "total_tokens": 21146,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 16651.53,
+  "sql_execution_elapsed_ms_total": 4.03,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f158fa312fc49119/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..33485765e43777ff6a9747f38f61d28196e749f2
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_hp.
+Result preview: [{"class": "1", "word_freq_hpl": "0", "total_measure": 10.99, "share_within_group": 34.67970968759861}, {"class": "0", "word_freq_hpl": "0", "total_measure": 863.15, "share_within_group": 34.5732962692964}, {"class": "1", "word_freq_hpl": "0.39", "total_measure": 3.58, "share_within_group": 11.2969390975071}, {"class": "1", "word_freq_hpl": "1.77", "total_measure": 3.37, "share_within_group": 10.634269485642157}, {"class": "1", "word_freq_hpl": "1.49", "total_measure": 1.49, "share_within_group": 4.701798674660776}] Results were truncated.
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..8b562dab7f59a9124a674096a8f22c4fbb92c2b3
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/generated_sql.sql
@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n1_f327357bd231f2f8
+-- problem_id: v2p_n1_716f94ffe1145fb8
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "class",
+  "word_freq_hpl",
+  SUM(CAST("word_freq_hp" AS REAL)) AS total_measure,
+  SUM(CAST("word_freq_hp" AS REAL)) * 100.0 / SUM(SUM(CAST("word_freq_hp" AS REAL))) OVER (PARTITION BY "class") AS share_within_group
+FROM "n1"
+GROUP BY "class", "word_freq_hpl"
+ORDER BY share_within_group DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7fe4f076d2f5e750e5208fd3d3cad8bc60d6b096
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"class\",\n  \"word_freq_hpl\",\n  SUM(CAST(\"word_freq_hp\" AS REAL)) AS total_measure,\n  SUM(CAST(\"word_freq_hp\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_hp\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_hpl\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_hpl\\\",\\n  SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hp\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_hpl\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"class\", \"word_freq_hpl\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"class\": \"1\", \"word_freq_hpl\": \"0\", \"total_measure\": 10.99, \"share_within_group\": 34.67970968759861}, {\"class\": \"0\", \"word_freq_hpl\": \"0\", \"total_measure\": 863.15, \"share_within_group\": 34.5732962692964}, {\"class\": \"1\", \"word_freq_hpl\": \"0.39\", \"total_measure\": 3.58, \"share_within_group\": 11.2969390975071}, {\"class\": \"1\", \"word_freq_hpl\": \"1.77\", \"total_measure\": 3.37, \"share_within_group\": 10.634269485642157}, {\"class\": \"1\", \"word_freq_hpl\": \"1.49\", \"total_measure\": 1.49, \"share_within_group\": 4.701798674660776}, {\"class\": \"1\", \"word_freq_hpl\": \"1.46\", \"total_measure\": 1.46, \"share_within_group\": 4.607131587251499}, {\"class\": \"1\", \"word_freq_hpl\": \"1.35\", \"total_measure\": 1.35, \"share_within_group\": 4.260018933417482}, {\"class\": \"1\", \"word_freq_hpl\": \"1.19\", \"total_measure\": 1.19, \"share_within_group\": 3.7551278005680024}, {\"class\": \"1\", \"word_freq_hpl\": \"1.22\", \"total_measure\": 1.18, \"share_within_group\": 3.72357210476491}, {\"class\": \"1\", \"word_freq_hpl\": \"0.98\", \"total_measure\": 0.98, \"share_within_group\": 3.092458188703061}, {\"class\": \"1\", \"word_freq_hpl\": \"0.97\", \"total_measure\": 0.97, \"share_within_group\": 3.0609024928999684}, {\"class\": \"0\", \"word_freq_hpl\": \"4.76\", \"total_measure\": 57.12, \"share_within_group\": 2.287929888086903}, {\"class\": \"0\", \"word_freq_hpl\": \"4.16\", \"total_measure\": 55.66, \"share_within_group\": 2.2294498874460262}, {\"class\": \"1\", \"word_freq_hpl\": \"0.7\", \"total_measure\": 0.7, \"share_within_group\": 2.208898706216472}, {\"class\": \"1\", \"word_freq_hpl\": \"1.36\", \"total_measure\": 0.68, \"share_within_group\": 2.145787314610287}, {\"class\": \"1\", \"word_freq_hpl\": \"0.57\", \"total_measure\": 0.57, \"share_within_group\": 1.7986746607762698}, {\"class\": \"1\", \"word_freq_hpl\": \"0.52\", \"total_measure\": 0.52, \"share_within_group\": 1.6408961817608079}, {\"class\": \"1\", \"word_freq_hpl\": \"0.51\", \"total_measure\": 0.51, \"share_within_group\": 1.6093404859577154}, {\"class\": \"1\", \"word_freq_hpl\": \"0.44\", \"total_measure\": 0.44, \"share_within_group\": 1.388450615336068}, {\"class\": \"0\", \"word_freq_hpl\": \"7.69\", \"total_measure\": 30.76, \"share_within_group\": 1.2320854929543616}, {\"class\": \"0\", \"word_freq_hpl\": \"3.57\", \"total_measure\": 28.56, \"share_within_group\": 1.1439649440434514}, {\"class\": \"1\", \"word_freq_hpl\": \"0.36\", \"total_measure\": 0.36, \"share_within_group\": 1.1360050489113285}, {\"class\": \"0\", \"word_freq_hpl\": \"9.09\", \"total_measure\": 27.27, \"share_within_group\": 1.0922942585456905}, {\"class\": \"1\", \"word_freq_hpl\": \"0.34\", \"total_measure\": 0.34, \"share_within_group\": 1.0728936573051435}, {\"class\": \"0\", \"word_freq_hpl\": \"4.34\", \"total_measure\": 26.07, \"share_within_group\": 1.044228504594285}, {\"class\": \"0\", \"word_freq_hpl\": \"0.81\", \"total_measure\": 26.06, \"share_within_group\": 1.04382795664469}, {\"class\": \"0\", \"word_freq_hpl\": \"3.44\", \"total_measure\": 24.97, \"share_within_group\": 1.00016823013883}, {\"class\": \"1\", \"word_freq_hpl\": \"0.26\", \"total_measure\": 0.26, \"share_within_group\": 0.8204480908804039}, {\"class\": \"0\", \"word_freq_hpl\": \"2.77\", \"total_measure\": 19.419999999999998, \"share_within_group\": 0.7778641181135794}, {\"class\": \"0\", \"word_freq_hpl\": \"2.63\", \"total_measure\": 19.28, \"share_within_group\": 0.7722564468192488}, {\"class\": \"0\", \"word_freq_hpl\": \"3.84\", \"total_measure\": 19.21, \"share_within_group\": 0.7694526111720834}, {\"class\": \"0\", \"word_freq_hpl\": \"4\", \"total_measure\": 19.14, \"share_within_group\": 0.7666487755249181}, {\"class\": \"0\", \"word_freq_hpl\": \"2.56\", \"total_measure\": 17.93, \"share_within_group\": 0.7181824736239175}, {\"class\": \"1\", \"word_freq_hpl\": \"0.21\", \"total_measure\": 0.21, \"share_within_group\": 0.6626696118649416}, {\"class\": \"1\", \"word_freq_hpl\": \"0.05\", \"total_measure\": 0.2, \"share_within_group\": 0.6311139160618492}, {\"class\": \"0\", \"word_freq_hpl\": \"2.35\", \"total_measure\": 15.75, \"share_within_group\": 0.6308630206121975}, {\"class\": \"0\", \"word_freq_hpl\": \"2.04\", \"total_measure\": 13.26, \"share_within_group\": 0.531126581163031}, {\"class\": \"0\", \"word_freq_hpl\": \"1.28\", \"total_measure\": 13.16, \"share_within_group\": 0.5271211016670806}, {\"class\": \"0\", \"word_freq_hpl\": \"1.19\", \"total_measure\": 13.15, \"share_within_group\": 0.5267205537174855}, {\"class\": \"0\", \"word_freq_hpl\": \"2\", \"total_measure\": 12.0, \"share_within_group\": 0.48065753951405527}, {\"class\": \"0\", \"word_freq_hpl\": \"2.17\", \"total_measure\": 11.94, \"share_within_group\": 0.47825425181648495}, {\"class\": \"0\", \"word_freq_hpl\": \"2.98\", \"total_measure\": 11.93, \"share_within_group\": 0.4778537038668899}, {\"class\": \"0\", \"word_freq_hpl\": \"0.1\", \"total_measure\": 11.92, \"share_within_group\": 0.47745315591729487}, {\"class\": \"0\", \"word_freq_hpl\": \"2.22\", \"total_measure\": 11.65, \"share_within_group\": 0.4666383612782286}, {\"class\": \"0\", \"word_freq_hpl\": \"2.32\", \"total_measure\": 11.620000000000001, \"share_within_group\": 0.4654367174294435}, {\"class\": \"0\", \"word_freq_hpl\": \"2.23\", \"total_measure\": 11.36, \"share_within_group\": 0.4550224707399723}, {\"class\": \"0\", \"word_freq_hpl\": \"4.05\", \"total_measure\": 11.25, \"share_within_group\": 0.4506164432944268}, {\"class\": \"0\", \"word_freq_hpl\": \"1.2\", \"total_measure\": 11.2, \"share_within_group\": 0.4486137035464516}, {\"class\": \"0\", \"word_freq_hpl\": \"1.06\", \"total_measure\": 10.97, \"share_within_group\": 0.4394011007057655}, {\"class\": \"0\", \"word_freq_hpl\": \"5.4\", \"total_measure\": 10.8, \"share_within_group\": 0.4325917855626497}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 10.18}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..fb64b3df32467d081f841f967c8e9575a8f21103
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/run_manifest.json
@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:33:57.502956+00:00",
+  "ended_at": "2026-05-19T15:34:08.875962+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_f327357bd231f2f8",
+    "problem_id": "v2p_n1_716f94ffe1145fb8",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_hp.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_hp",
+      "item_col": "word_freq_hpl",
+      "top_k": 14,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=24"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_f327357bd231f2f8.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f327357bd231f2f8",
+    "api_calls": 0,
+    "input_tokens": 20436,
+    "cached_input_tokens": 12032,
+    "output_tokens": 606,
+    "total_tokens": 21042,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11353.46,
+    "sql_execution_elapsed_ms_total": 10.18,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd28bd13cdc2d22f4a08e43a5f45190f4d7dc287
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:34:08.862399+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11353.46, "started_at": "2026-05-19T15:33:57.507132+00:00", "ended_at": "2026-05-19T15:34:08.860637+00:00", "prompt_metrics": {"chars": 29767, "bytes_utf8": 29767, "lines": 794, "estimated_tokens": null}, "response_metrics": {"chars": 626, "bytes_utf8": 626, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 12032, "output_tokens": 606, "reasoning_output_tokens": 428}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"word_freq_hpl\\\",\\n  SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hp\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_hpl\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_hpl\\\", and measure_col=\\\"word_freq_hp\\\". Cast the measure to REAL because the SQLite schema stores these frequency fields as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..733f7d13a6e5c2caebe50ca38e5eea5800215ea7
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_f327357bd231f2f8",
+  "api_calls": 0,
+  "input_tokens": 20436,
+  "cached_input_tokens": 12032,
+  "output_tokens": 606,
+  "total_tokens": 21042,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11353.46,
+  "sql_execution_elapsed_ms_total": 10.18,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f65b05dcfd0a0758f50436490f5d9ce74a6bc19
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29529, "bytes_utf8": 29529, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 788, "bytes_utf8": 788, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 12032, "output_tokens": 1520, "reasoning_output_tokens": 1288}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..7d0a746a07fac9eca4efa9eb867d47efac7a618e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f3a5c50681bb4e5d",
+    "api_calls": 0,
+    "input_tokens": 20355,
+    "cached_input_tokens": 12032,
+    "output_tokens": 1520,
+    "total_tokens": 21875,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 24595.95,
+    "sql_execution_elapsed_ms_total": 17.64,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8f9749009851c8e4ffe6f410d0e905f2dd06bc6
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:56:23.697645+00:00",
+  "ended_at": "2026-05-19T15:56:48.293628+00:00",
+  "elapsed_ms": 24595.95,
+  "prompt_metrics": {
+    "chars": 29529,
+    "bytes_utf8": 29529,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1211,
+    "bytes_utf8": 1211,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 788,
+      "bytes_utf8": 788,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20355,
+      "cached_input_tokens": 12032,
+      "output_tokens": 1520,
+      "reasoning_output_tokens": 1288
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..77d7bef9b2ef234d175f2dc8d88d5c5f1f30fbb9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_85.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_85",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ebb2fca4876a9556101e8aa5258f5a80e7a13d49
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f3-dc1c-78f2-b5fd-19bd057a32f9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"class\\\",\\n    CAST(\\\"word_freq_85\\\" AS REAL) AS \\\"measure_value\\\",\\n    CUME_DIST() OVER (\\n      PARTITION BY \\\"class\\\"\\n      ORDER BY CAST(\\\"word_freq_85\\\" AS REAL)\\n    ) AS \\\"cume_dist\\\"\\n  FROM \\\"n1\\\"\\n  WHERE \\\"class\\\" IS NOT NULL\\n    AND \\\"word_freq_85\\\" IS NOT NULL\\n)\\nSELECT\\n  \\\"class\\\",\\n  MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"cume_dist\\\" >= 0.9\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST() to return the nearest percentile point at or above the 90th percentile for each \\\"class\\\" group. \\\"word_freq_85\\\" is cast from TEXT to REAL for numeric ordering.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":12032,"output_tokens":1520,"reasoning_output_tokens":1288}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..323dd89d73936f1c4123f23a7230e4577bc7bd3a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"class\",\n    CAST(\"word_freq_85\" AS REAL) AS \"measure_value\",\n    CUME_DIST() OVER (\n      PARTITION BY \"class\"\n      ORDER BY CAST(\"word_freq_85\" AS REAL)\n    ) AS \"cume_dist\"\n  FROM \"n1\"\n  WHERE \"class\" IS NOT NULL\n    AND \"word_freq_85\" IS NOT NULL\n)\nSELECT\n  \"class\",\n  MIN(\"measure_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"cume_dist\" >= 0.9\nGROUP BY \"class\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST() to return the nearest percentile point at or above the 90th percentile for each \"class\" group. \"word_freq_85\" is cast from TEXT to REAL for numeric ordering."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3a5c50681bb4e5d/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a837974bce1f77161c59f70641f61ccef86c2b3d
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29525, "bytes_utf8": 29525, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 431, "bytes_utf8": 431, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20362, "cached_input_tokens": 12032, "output_tokens": 404, "reasoning_output_tokens": 281}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..07783636a89c3c961bf9c9d1675b5d69f4c7b18c
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f3e02b5cab2bf044",
+    "api_calls": 0,
+    "input_tokens": 20362,
+    "cached_input_tokens": 12032,
+    "output_tokens": 404,
+    "total_tokens": 20766,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9943.2,
+    "sql_execution_elapsed_ms_total": 3.44,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..61938b1b6de3457e496bba3b695cc0761e906b76
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:05:20.078187+00:00",
+  "ended_at": "2026-05-19T16:05:30.021428+00:00",
+  "elapsed_ms": 9943.2,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 791,
+    "bytes_utf8": 791,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 431,
+      "bytes_utf8": 431,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20362,
+      "cached_input_tokens": 12032,
+      "output_tokens": 404,
+      "reasoning_output_tokens": 281
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e1753e45efc2f885e7e6e5ae8bd44f72e4d1af8
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_000.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_000",
+    "top_k": 16,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd2ea8aab621621eefe1211da38696ee37de4205
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fc-0b6f-7d52-8755-28765beff531"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_000\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_000\\\" AS REAL)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 16;\",\"notes\":\"Uses the planned template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_000\\\". CAST to REAL is added because the schema stores these numeric values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20362,"cached_input_tokens":12032,"output_tokens":404,"reasoning_output_tokens":281}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..72666157671557a00a46be36c3e572dd81ef54d4
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_000\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_000\" AS REAL)) > 0.0\nORDER BY total_measure DESC\nLIMIT 16;","notes":"Uses the planned template with group_col=\"class\" and measure_col=\"word_freq_000\". CAST to REAL is added because the schema stores these numeric values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f3e02b5cab2bf044/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..839ec79294f8445abad7619ab86ebdbb3f1a8318
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/run_manifest.json
@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:04:33.536787+00:00",
+  "ended_at": "2026-05-19T16:04:43.931528+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_f72544bb07dd6eb1",
+    "problem_id": "v2p_n1_8272e415ad215673",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_your.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_your",
+      "top_k": 19,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.94,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=2/2",
+      "binding_index=134"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06b4b21a3eb150f311c235591bd48ab9e1980326
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/trace.jsonl
@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:04:40.013543+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 6473.45, "started_at": "2026-05-19T16:04:33.539079+00:00", "ended_at": "2026-05-19T16:04:40.012554+00:00", "prompt_metrics": {"chars": 29528, "bytes_utf8": 29528, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-5593-7ed1-97a1-52362d9306fc\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:04:43.931429+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2915.65, "started_at": "2026-05-19T16:04:41.014863+00:00", "ended_at": "2026-05-19T16:04:43.930542+00:00", "prompt_metrics": {"chars": 29528, "bytes_utf8": 29528, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-72d2-7092-ad24-3395898b7360\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0f32c2b53f8f76a2226ace51031cebbe0dc3d4e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl
@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29917, "bytes_utf8": 29917, "lines": 792, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 646, "bytes_utf8": 646, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 470, "reasoning_output_tokens": 304}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/session_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..efc12d6a91fd78d144a0a1ce476a1419e967ffdf
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/session_summary.json
@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f9b1c96b1ca12ac2",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 470,
+    "total_tokens": 20923,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15565.6,
+    "sql_execution_elapsed_ms_total": 5.35,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_attempt_1.metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..72d3e4d1d49295bfba56a35f87b7960c59ab210b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_attempt_1.metadata.json
@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:48:25.766568+00:00",
+  "ended_at": "2026-05-19T15:48:41.332217+00:00",
+  "elapsed_ms": 15565.6,
+  "prompt_metrics": {
+    "chars": 29917,
+    "bytes_utf8": 29917,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1346,
+    "bytes_utf8": 1346,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 646,
+      "bytes_utf8": 646,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20453,
+      "cached_input_tokens": 19840,
+      "output_tokens": 470,
+      "reasoning_output_tokens": 304
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_prompt_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2b289e295c30567de9ea08e0fa35ca30ba4a42fc
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_prompt_attempt_1.txt
@@ -0,0 +1,792 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_credit.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_credit",
+    "top_k": 16,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.raw.txt
new file mode 100644
index 0000000000000000000000000000000000000000..34d17eb8092efe354096ff9d0a1c1a3b95ce1463
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.raw.txt
@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40ec-9135-7413-adb2-d12e461822c5"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1988301, Requested 21088. Please try again in 281ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_credit\\\". CAST to REAL is used because the schema stores numeric feature values as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":470,"reasoning_output_tokens":304}}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6ad7dce656866aef951b55e76c1ee968fef2830
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_response_attempt_1.txt
@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_credit\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_credit\". CAST to REAL is used because the schema stores numeric feature values as TEXT."}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/sql_stderr_attempt_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/final_answer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c44374eaba7b3cdf86679e1f14896facce88146a
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/final_answer.txt
@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_credit.
+Result preview: [{"class": "1", "group_value": 372.61}, {"class": "0", "group_value": 21.13}]
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/generated_sql.sql
new file mode 100644
index 0000000000000000000000000000000000000000..4751bbdc01bc14906d290adb0fe5414ea410566b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/generated_sql.sql
@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_n1_f9b1c96b1ca12ac2
+-- problem_id: v2p_n1_0b5c6a9d5713c6d2
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "class", SUM(CAST("word_freq_credit" AS REAL)) AS group_value
+    FROM "n1"
+    GROUP BY "class"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."class", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/query_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92c4e375060ccbedf2ec7ad7c47c363c74d1501b
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/query_results.jsonl
@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"class\", SUM(CAST(\"word_freq_credit\" AS REAL)) AS group_value\n    FROM \"n1\"\n    GROUP BY \"class\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"class\", \"group_value\"], \"rows\": [{\"class\": \"1\", \"group_value\": 372.61}, {\"class\": \"0\", \"group_value\": 21.13}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.35}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/run_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c18f82f0a97481ef18949e253e8a906fbd403b9e
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/run_manifest.json
@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:48:25.763781+00:00",
+  "ended_at": "2026-05-19T15:48:41.342793+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_f9b1c96b1ca12ac2",
+    "problem_id": "v2p_n1_0b5c6a9d5713c6d2",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_credit.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_credit",
+      "top_k": 16,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=2/2",
+      "binding_index=76"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_f9b1c96b1ca12ac2.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_f9b1c96b1ca12ac2",
+    "api_calls": 0,
+    "input_tokens": 20453,
+    "cached_input_tokens": 19840,
+    "output_tokens": 470,
+    "total_tokens": 20923,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15565.6,
+    "sql_execution_elapsed_ms_total": 5.35,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}
\ No newline at end of file
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/trace.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b0b128e2bf415b749ea0ca79017a3449167e4fc0
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/trace.jsonl
@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:48:41.333910+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15565.6, "started_at": "2026-05-19T15:48:25.766568+00:00", "ended_at": "2026-05-19T15:48:41.332217+00:00", "prompt_metrics": {"chars": 29917, "bytes_utf8": 29917, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 646, "bytes_utf8": 646, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 470, "reasoning_output_tokens": 304}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS group_value\\n    FROM \\\"n1\\\"\\n    GROUP BY \\\"class\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_credit\\\". CAST to REAL is used because the schema stores numeric feature values as TEXT.\"}"}
diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/usage_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..4ad7fb3b0b3b88c1639576dae77f3efc8b29adc9
--- /dev/null
+++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/usage_summary.json
@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_f9b1c96b1ca12ac2",
+  "api_calls": 0,
+  "input_tokens": 20453,
+  "cached_input_tokens": 19840,
+  "output_tokens": 470,
+  "total_tokens": 20923,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 15565.6,
+  "sql_execution_elapsed_ms_total": 5.35,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f9b1c96b1ca12ac2/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}
\ No newline at end of file